% This file was created with JabRef 2.3.1.
% Encoding: ISO8859_1

@ARTICLE{BadriSOL03,
  author = {S. Abolmaali and C. Ostermann and A. Zell},
  title = {The Compressed Feature Matrix - a Novel Descriptor for Adaptive Similarity
	Search},
  journal = {J. Mol. Model.},
  year = {2003},
  volume = {9},
  pages = {66 - 75}
}

@ARTICLE{adryan04,
  author = {B. Adryan and R. Schuh},
  title = {{G}ene {O}ntology-based clustering of gene expression data},
  journal = {To appear in {B}ioinformatics},
  year = {2004}
}

@ARTICLE{Agrafiotis2003MolSim,
  author = {D. Agrafiotis and H. Xu},
  title = {{A Geodisc Framework for Analyzing Molecular Similarities}},
  journal = {J. Chem. Inf. Comp. Sci.},
  year = {2003},
  volume = {43},
  pages = {475 - 484},
  owner = {holger},
  timestamp = {2006.10.15}
}

@ARTICLE{AhoTransRed1972,
  author = {A. Aho and M. Garey and J. Ullman},
  title = {The Transitive Reduction of a Directed Graph},
  journal = {SIAM Journal on Computing},
  year = {1972},
  volume = {1},
  pages = {131 - 137},
  number = {2},
  owner = {froehlih},
  timestamp = {2007.04.16}
}

@ARTICLE{AizKernelTrick64,
  author = {M. Aizerman and E. Braverman and L. Rozonoer},
  title = {Theoretical foundations of the potential function method in pattern
	recognition learning},
  journal = {Automation and Remote Control},
  year = {1964},
  volume = {25},
  pages = {821 - 837}
}

@CONFERENCE{akahoKCCA01,
  author = {S. Akaho},
  title = {A kernel method for canonical correlation analysis},
  booktitle = {Proc. Int. Meeting Psychometric Society},
  year = {2001}
}

@ARTICLE{Alexa2006topGO,
  author = {Adrian Alexa and J\"org Rahnenf�hrer and Thomas Lengauer},
  title = {{Improved scoring of functional groups from gene expression data
	by decorrelating GO graph structure}},
  journal = {Bioinformatics},
  year = {2006},
  volume = {22},
  pages = {1600 - 1607},
  number = {13},
  owner = {froehlih},
  timestamp = {2007.12.04}
}

@ARTICLE{Lymphoma2000,
  author = {ASH A. ALIZADEH and MICHAEL B. EISEN and R. ERIC DAVIS and CHI M
	and, IZIDORE S. LOSSOS and ANDREAS ROSENWALD and
	
	 JENNIFER C. BOLDRICK and HAJEER SABET and TRUC TRAN and XIN YU and
	JOHN I. POWELL and LIMING YANG and GERALD E. MARTI and 
	
	 TROY MOORE and JAMES HUDSON JR and LISHENG LU and DAVID B. LEWIS
	and ROBERT TIBSHIRANI and GAVIN SHERLOCK and WING C. CHAN and
	
	 TIMOTHY C. GREINER and DENNIS D. WEISENBURGER and JAMES O. ARMITAGE
	and ROGER WARNKE and RONALD LEVY and 
	
	 WYNDHAM WILSON and MICHAEL R. GREVER and JOHN C. BYRD and DAVID BOTSTEIN
	and PATRICK O. BROWN and LOUIS M. STAUDT},
  title = {Distinct types of diffuse large B-cell lymphoma identified by gene
	expression profiling},
  journal = {Nature},
  year = {2000},
  volume = {403},
  pages = {503 - 511}
}

@ARTICLE{AloBar99,
  author = {U. Alon and N. Barkai and D. Notterman and K. Gish and S. Ybarra
	and D. Mack and A. Levine},
  title = {Broad patterns of gene expression revealed by clustering analysis
	of tumor and normal colon cancer tissues probed by oligonucleotide
	arrays},
  journal = {Cell Biology},
  year = {1999},
  volume = {96},
  pages = {6745 - 6750}
}

@ARTICLE{Alroy1997,
  author = {I. Alroy and Y. Yarden},
  title = {The ErbB signaling network in embryogenesis and oncogenesis: signal
	diversification through combinatorial ligand-receptor interactions.},
  journal = {FEBS Lett},
  year = {1997},
  volume = {410},
  pages = {83--86},
  number = {1},
  month = {Jun},
  abstract = {Ligand-induced activation of receptor tyrosine kinases (RTK) results
	in the initiation of diverse cellular pathways, including proliferation,
	differentiation and cell migration. The ErbB family of RTKs represents
	a model for signal diversification through the formation of homo-
	and heterodimeric receptor complexes. Each dimeric receptor complex
	will initiate a distinct signaling pathway by recruiting a different
	set of Src homology 2- (SH2-) containing effector proteins. Further
	complexity is added due to the existence of an oncogenic receptor
	that enhances and stabilizes dimerization but has no ligand (ErbB-2),
	and a receptor that can recruit novel SH-2-containing proteins, but
	is itself devoid of kinase activity (ErbB-3). The resulting signaling
	network has important implications for embryonic development and
	malignant transformation.},
  keywords = {Animals; Cell Transformation, Neoplastic; Embryonic and Fetal Development;
	Epidermal Growth Factor; Humans; Ligands; Proto-Oncogene Proteins;
	Receptor, Epidermal Growth Factor; Receptor, erbB-2; Receptor, erbB-3;
	Signal Transduction},
  owner = {froehlih},
  pii = {S0014-5793(97)00412-2},
  pmid = {9247128},
  timestamp = {2008.10.23}
}

@ARTICLE{Anchang2009,
  author = {Benedict Anchang and Mohammad J Sadeh and Juby Jacob and Achim Tresch
	and Marcel O Vlad and Peter J Oefner and Rainer Spang},
  title = {Modeling the temporal interplay of molecular signaling and gene expression
	by using dynamic nested effects models.},
  journal = {Proc Natl Acad Sci U S A},
  year = {2009},
  volume = {106},
  pages = {6447--6452},
  number = {16},
  month = {Apr},
  abstract = {Cellular decision making in differentiation, proliferation, or cell
	death is mediated by molecular signaling processes, which control
	the regulation and expression of genes. Vice versa, the expression
	of genes can trigger the activity of signaling pathways. We introduce
	and describe a statistical method called Dynamic Nested Effects Model
	(D-NEM) for analyzing the temporal interplay of cell signaling and
	gene expression. D-NEMs are Bayesian models of signal propagation
	in a network. They decompose observed time delays of multiple step
	signaling processes into single steps. Time delays are assumed to
	be exponentially distributed. Rate constants of signal propagation
	are model parameters, whose joint posterior distribution is assessed
	via Gibbs sampling. They hold information on the interplay of different
	forms of biological signal propagation. Molecular signaling in the
	cytoplasm acts at high rates, direct signal propagation via transcription
	and translation act at intermediate rates, while secondary effects
	operate at low rates. D-NEMs allow the dissection of biological processes
	into signaling and expression events, and analysis of cellular signal
	flow. An application of D-NEMs to embryonic stem cell development
	in mice reveals a feed-forward loop dominated network, which stabilizes
	the differentiated state of cells and points to Nanog as the key
	sensitizer of stem cells for differentiation stimuli.},
  doi = {10.1073/pnas.0809822106},
  institution = {Institute of Functional Genomics, University of Regensburg, Josef-Engert-Strasse
	9, 93053 Regensburg, Germany.},
  keywords = {Algorithms; Animals; Gene Expression Regulation; Mice; Models, Genetic;
	Signal Transduction; Stem Cells; Time Factors},
  owner = {frohlich},
  pii = {0809822106},
  pmid = {19329492},
  timestamp = {2012.01.30},
  url = {http://dx.doi.org/10.1073/pnas.0809822106}
}

@ARTICLE{AroRKHS50,
  author = {N. Aronszajn},
  title = {Theory of reproducing kernels},
  journal = {Trans. Am. Math. Soc.},
  year = {1950},
  volume = {68},
  pages = {337 - 404}
}

@INCOLLECTION{Waterbeemd03Book,
  author = {P. Artursson and C. Bergstr\"om},
  title = {Intestinal Absorption: The Role of Polar Surface Area},
  booktitle = {Drug Bioavailability},
  publisher = {Wiley-VCH},
  year = {2003},
  editor = {H. van de Waterbeemd and H. Lennern\"as and P. Artursson},
  pages = {341 - 357},
  address = {Weinheim}
}

@BOOK{BoeSchnBook00,
  title = {Virtual Screening for Bioactive Molecules},
  publisher = {Wiley-VCH},
  year = {2000},
  author = {H.-J. B\"ohm and G. Schneider},
  address = {Weinheim}
}

@ARTICLE{BoehmKlebeHydro02,
  author = {M. B\"ohm and G. Klebe},
  title = {Development of a New Hydrogen-Bond Descriptor and Their Application
	to Comparative Mean Field Analysis},
  journal = {J. Med. Chem.},
  year = {2002},
  volume = {45},
  pages = {1585 - 1597}
}

@ARTICLE{KlebeDescs02,
  author = {M. B\"ohm and G. Klebe},
  title = {{D}evelopment of {N}ew {H}ydrogen--{B}ond {D}escriptors and {T}heir
	{A}pplication to {C}omparative {M}olecular {F}ield {A}nalyses},
  journal = {J. Med. Chem.},
  year = {2002},
  volume = {45},
  pages = {1585--1597},
  abstract = {Knowledge-based descriptors extracted from composite crystal-field
	environments in crystal data have been developed for the description
	of interaction properties of small molecules. Using SuperStar seven
	diverse probe atoms have been selected to reflect the most important
	physicochemical properties. The general application of these descriptors
	in comparative molecular field analysis has been investigated using
	a dataset of thermolysin inhibitors, and a comparison to the GRID
	program has been performed. We especially focused on hydrogen-bond
	donor and acceptor properties by selecting a carbonyl and amino group
	as suitable probes. Their performance has been compared to that of
	the hydrogen-bond descriptors presently implemented in CoMSIA (comparative
	molecular similarity indices analysis). The newly developed descriptors
	produced significantly improved statistics for the correlation analyses
	if they are exclusively applied or, even better, applied in c ombination
	with other CoMSIA descriptors. Two methodologically different approaches
	have been tested to approximate the developed descriptors. Both reduce
	significantly the required computational efforts in particular for
	large data sets. The graphical interpretation of the field contributions
	of hydrogen-bonding properties elucidates additional features compared
	to those obtained from the original CoMSIA method. They are of valuable
	support for the design of improved inhibitors.},
  groupsearch = {0}
}

@ARTICLE{bachKCCAandKICA02,
  author = {F. Bach and M. Jordan},
  title = {Kernel independent component analysis},
  journal = {J. Machine Learning Research},
  year = {2002},
  volume = {3},
  pages = {1 - 48}
}

@ARTICLE{BalonHIA99,
  author = {K. Balon and B. Riebesehl and B. M\"uller},
  title = {Drug Liposome Partitioning as a Tool for the Prediction of Human
	Passive Intestinal Absorption},
  journal = {Pharm. Res.},
  year = {1999},
  volume = {16},
  pages = {882 - 888}
}

@ARTICLE{BarthHerz2006,
  author = {Andreas S. Barth and and Ruprecht Kuner and Andreas Buness and Markus
	Ruschhaupt and Sylvia Merk and Ludwig Zwermann and Stefan K\"a\"ab
	and Eckart Kreuzer and Gerhard Steinbeck and Ulrich Mansmann and
	Annemarie Poustka and Michael Nabauer and Holger S\"ultmann},
  title = {Identification of a Common Gene Expression Signature in Dilated Cardiomyopathy
	Across Independent Microarray Studies},
  journal = {Journal of the American College of Cardiology},
  year = {2006},
  volume = {48},
  pages = {1610 - 1617},
  number = {8},
  owner = {froehlih},
  timestamp = {2006.10.30}
}

@INCOLLECTION{BartlettTaylorSVMBound99,
  author = {P. Bartlett and J. Shawe-Taylor},
  title = {Generalization performance of support vector machines and other pattern
	classifiers},
  booktitle = {Advances in Kernel Methods - Support Vector Learning},
  publisher = {MIT Press},
  year = {1999},
  editor = {B. Sch\"olkopf and C. Burges and A. Smola},
  pages = {43 - 54},
  address = {Cambridge, MA}
}

@ARTICLE{BattitiMIFS94,
  author = {R. Battiti},
  title = {Using Mutual Information For Selecting Features in Supervised Neural
	Net Learning},
  journal = {IEEE Trans. Neural Networks},
  year = {1994},
  volume = {5},
  pages = {537 - 550},
  number = {4}
}

@ARTICLE{BaumWelch70HMMAlgo,
  author = {L. E. Baum and T. Peterie and G. Souled and N. Weiss},
  title = {{A maximization technique occurring in the statistical analysis of
	probabilistic functions of Markov chains}},
  journal = {Ann. Math. Statist.},
  year = {1970},
  volume = {41},
  pages = {164 - 171},
  number = {1}
}

@ARTICLE{beissbarth04,
  author = {T. Beissbarth and T. Speed},
  title = {{GO}stat: finding statistically overexpressed {G}ene {O}ntologies
	within groups of genes},
  journal = {Bioinformatics},
  year = {2004},
  volume = {20},
  pages = {1464-1465},
  number = {9},
  doi = {10.1093/bioinformatics/bth088}
}

@ARTICLE{Belisle1992SimAnn,
  author = {Belisle, C. J. P.},
  title = {Convergence theorems for a class of simulated annealing algorithms},
  journal = {J. Applied Probability},
  year = {1992},
  volume = {29},
  pages = {885 - 895},
  owner = {froehlih},
  timestamp = {2006.11.28}
}

@TECHREPORT{BelkinSemisup04,
  author = {M. Belkin and P. Niyogi and V. Sindhwani},
  title = {Manifold Regularization: A Geometric Framework for Learning from
	Examples},
  institution = {Dept. of Computer Science, University of Chicago},
  year = {2004},
  number = {TR-2003-06}
}

@ARTICLE{Belozertseva2006FST,
  author = {Belozertseva, I.V. and Kos, T. and Popik, P. and Danysz, W. and Bespalov,
	A.Y.},
  title = {Antidepressant-like effects of mGluR1 and mGlu5 antagonists in the
	rat forced swim and the mouse tail suspension test},
  journal = {Eur. Neuropsychopharmacol.},
  year = {2006},
  note = {Epub ahead of print.},
  owner = {froehlih},
  timestamp = {2006.07.13}
}

@ARTICLE{BenHur05ProtProtInteraction,
  author = {A. Ben-Hur and W. Noble},
  title = {{Kernel Methods for Predicting Protein-Protein Interactions}},
  journal = {Bioinformatics},
  year = {2005},
  volume = {21},
  pages = {i38 - i46},
  number = {1}
}

@ARTICLE{BenderMolSim04,
  author = {A. Bender and R. Glen},
  title = {Molecular similarity: a key technique in molecular informatics},
  journal = {Org. Biomol. Chem.},
  year = {2004},
  volume = {2},
  pages = {3204 - 3218}
}

@ARTICLE{Benjamini1995FDRControl,
  author = {Benjamini, Y. and Hochberg, Y.},
  title = {{Controlling the False Discovery Rate: A Practical and Powerful Approach
	to Multiple Testing}},
  journal = {J. Royal Statist. Soc., Series B},
  year = {1995},
  volume = {57},
  pages = {289 - 300},
  owner = {froehlih},
  timestamp = {2006.11.28}
}

@ARTICLE{Benjamini2001BY,
  author = {{Benjamini, Y. and Yekutieli, D.}},
  title = {{The control of the false discovery rate in multiple testing under
	dependency}},
  journal = {Annals of Statistics},
  year = {2001},
  volume = {29},
  pages = {1165 - 1188},
  owner = {froehlih},
  timestamp = {2008.04.08}
}

@BOOK{Berg2004MCMC,
  title = {Markov Chain Monte Carlo Simulations and Their Statistical Analysis},
  publisher = {World Scientific},
  year = {2004},
  author = {B. Berg},
  owner = {froehlih},
  timestamp = {2006.11.28}
}

@ARTICLE{beyer95,
  author = {Beyer, H.G.},
  title = {Toward a Theory of Evolution Strategies: On the Benefits of Sex -
	the $\mu/\mu,\lambda$ Theory},
  journal = {Evolutionary Computation},
  year = {1995},
  volume = {1},
  pages = {81-111},
  groupsearch = {0},
  keywords = {evolutionary algorithms}
}

@BOOK{BishopNN95,
  title = {Neural Networks for Pattern Recognition},
  publisher = {Clarendon Press},
  year = {1995},
  author = {C. Bishop},
  address = {Oxford}
}

@ARTICLE{BluLan97,
  author = {A. L. Blum and P. Langley},
  title = {{Selection of Relevant Features and Examples in Machine Learning}},
  journal = {Artificial Intelligence},
  year = {1997},
  volume = {97},
  pages = {245 - 271},
  number = {12}
}

@ARTICLE{Bolstad2003QuantNorm,
  author = {Bolstad, B. M. and Irizarry R. A. and Astrand, M. and Speed, T. P.},
  title = {A comparison of normalization methods for high density oligonucleotide
	array data based on bias and variance.},
  journal = {Bioinformatics},
  year = {2003},
  volume = {19},
  pages = {185-193},
  owner = {froehlih},
  timestamp = {2006.11.30}
}

@CONFERENCE{BonWei94,
  author = {B. Bonnlander and A. Weigend},
  title = {Selecting input variables using mutual information and nonparametric
	density estimation},
  booktitle = {Proc. 1994 Int. Symp. on Artificial Neural Networks},
  year = {1994},
  pages = {42 - 50}
}

@ARTICLE{Borsini1995FST,
  author = {Borsini, F.},
  title = {Role of the serotonergic system in the forced swimming test},
  journal = {Neurosci. Biobehav. Rev.},
  year = {1995},
  volume = {19},
  pages = {37-46},
  owner = {froehlih},
  timestamp = {2006.07.13}
}

@ARTICLE{Borsini1988FST,
  author = {Borsini, F. and Meli, A.},
  title = {Is the forced swimming test a suitable model for revealing antidepressant
	activity?},
  journal = {Psychopharmacology (Berl).},
  year = {1988},
  volume = {94},
  pages = {147-160},
  number = {2},
  owner = {froehlih},
  timestamp = {2006.07.14}
}

@CONFERENCE{BoserSVM92,
  author = {B. Boser and M. Guyon and V. Vapnik},
  title = {A training algorithm for optimal margin classifiers},
  booktitle = {Proc. 5th Ann. ACM Workshop on Comp. Learning Theory},
  year = {1992},
  editor = {D. Haussler},
  address = {Pittsburgh, PA},
  publisher = {ACM Press}
}

@ARTICLE{Boutros2002Data,
  author = {M. Boutros and H. Agaisse and N. Perrimon},
  title = {Sequential activation of signaling pathways during innate immune
	responses in \emph{Drosophila}},
  journal = {Developmental Cell},
  year = {2002},
  volume = {3},
  pages = {711 - 722},
  number = {5},
  owner = {froehlih},
  timestamp = {2006.11.28}
}

@ARTICLE{BradAUC97,
  author = {P. Bradley},
  title = {{The use of the are under the ROC curve in the evaluation of machine
	learning algorithms}},
  journal = {Pattern Recognition},
  year = {1997},
  volume = {30},
  pages = {1145 - 1159}
}

@CONFERENCE{BradleyFeatSelSVM98,
  author = {P. Bradley and O. Mangasarian},
  title = {Feature Selection via Concave Minimization and Support Vector Machines},
  booktitle = {Proc. 13th Int. Conf. Machine Learning},
  year = {1998},
  pages = {82 - 90}
}

@ARTICLE{BreimanRandomForests2001,
  author = {L. Breiman},
  title = {Random Forests},
  journal = {Machine Learning},
  year = {2001},
  volume = {45},
  pages = {5 - 32},
  number = {1},
  owner = {froehlih},
  timestamp = {2006.10.26}
}

@BOOK{BreimanCART84,
  title = {Classification and Regression Trees},
  publisher = {Wadsworth and Brooks},
  year = {1984},
  author = {L. Breiman and J. Friedman and R. Olshen and C. Stone}
}

@INCOLLECTION{budanitsky04,
  author = {A. Budanitsky and G. Hirst},
  title = {Semantic distance in {W}ord{N}et: {A}n experimental, application-oriented
	evaluation of five measures.},
  booktitle = {Workshop on {W}ord{N}et and other Lexical Resources, Second meeting
	of the {N}ord {A}merican Chapter of the Association for Computational
	Linguistics},
  year = {2001},
  address = {Pittsburgh}
}

@ARTICLE{Butt2005,
  author = {Alison J Butt and Catriona M McNeil and Elizabeth A Musgrove and
	Robert L Sutherland},
  title = {Downstream targets of growth factor and oestrogen signalling and
	endocrine resistance: the potential roles of c-Myc, cyclin D1 and
	cyclin E.},
  journal = {Endocr Relat Cancer},
  year = {2005},
  volume = {12 Suppl 1},
  pages = {S47--S59},
  month = {Jul},
  abstract = {Antioestrogen therapy is a highly effective treatment for patients
	with oestrogen-receptor (ER)-positive breast cancer, emphasising
	the central role of oestrogen action in the development and progression
	of this disease. However, effective antioestrogen treatment is often
	compromised by acquired endocrine resistance, prompting the need
	for a greater understanding of the down-stream mediators of oestrogen
	action that may contribute to this effect. Recent studies have demonstrated
	a critical link between oestrogen's mitogenic effects and cell cycle
	progression, particularly at the G1 to S transition where key effectors
	of oestrogen action are c-Myc and cyclin D1, which converge on the
	activation of cyclin E-cdk2. These components are rapidly upregulated
	in response to oestrogen, and can mimic its actions on cell cycle
	progression, including re-initiating cell proliferation in antioestrogen-arrested
	cells. Here we review the roles of c-Myc, cyclin D1 and cyclin E
	in oestrogen action and endocrine resistance, and identify their
	potential as markers of disease progression and endocrine responsiveness,
	and as novel therapeutic targets in endocrine-resistant breast cancer.},
  doi = {10.1677/erc.1.00993},
  keywords = {Breast Neoplasms; Cyclin D1; Cyclin E; Drug Resistance, Neoplasm;
	Estrogen Antagonists; Estrogens; Female; Growth Substances; Humans;
	Proto-Oncogene Proteins c-myc; Receptors, Estrogen; Signal Transduction},
  owner = {froehlih},
  pii = {12/Supplement_1/S47},
  pmid = {16113099},
  timestamp = {2008.10.23},
  url = {http://dx.doi.org/10.1677/erc.1.00993}
}

@ARTICLE{Schneider03,
  author = {E. Byvatov and U. Fechner and J. Sadowski and G. Schneider},
  title = {{Comparison of Support Vector Machine and Artificial Neural Network
	Systems for Drug/Nondrug Classification}},
  journal = {J. Chem. Inf. Comput. Sci.},
  year = {2003},
  volume = {43},
  pages = {1882 - 1889},
  number = {6}
}

@ARTICLE{ByvatovSVMDESCSEL04,
  author = {E. Byvatov and G. Schneider},
  title = {SVM-Based Feature Selection for Characterization of Focused Compound
	Collections},
  journal = {J. Chem. Inf. Comp. Sci.},
  year = {2004},
  volume = {44},
  pages = {993 - 999},
  number = {3}
}

@ARTICLE{CarhartAtomPair85,
  author = {R. Carhart and D. Smith and R. Venkataraghavan},
  title = {Atom Pairs as Molecular Features in Structure-Activity Studies: Definitionand
	Applications},
  journal = {J. Chem. Inf. Comput. Sci.},
  year = {1985},
  volume = {25},
  pages = {64 - 73}
}

@MANUAL{libsvm01,
  title = {{LIBSVM: a library for support vector machines}},
  author = {C. Chang and C. Lin},
  year = {2001},
  note = {Available from http://www.csie.ntu.edu.tw/$\sim$cjlin/libsvm}
}

@CONFERENCE{ChaVap00,
  author = {O. Chapelle and V. Vapnik},
  title = {{Model selection for Support Vector Machines}},
  booktitle = {Adv. Neural Inf. Proc. Syst. 12},
  year = {2000},
  editor = {S. Solla and T. Leen and K.-R. M\"uller},
  address = {Cambridge, MA},
  publisher = {MIT Press}
}

@ARTICLE{ChaVapBouMuk98,
  author = {O. Chapelle and V. Vapnik and O. Bousqet and S. Mukherjee},
  title = {{Choosing Multiple Parameters for Support Vector Machines}},
  journal = {Machine Learning},
  year = {2002},
  volume = {46},
  pages = {131 - 159},
  number = {1}
}

@CONFERENCE{ChapClustKer03,
  author = {O. Chapelle and J. Weston and B. Sch\"okopf},
  title = {Cluster Kernels for Semi-Supervised Learning},
  booktitle = {Neural Inf. Proc. Syst.},
  year = {2003}
}

@CONFERENCE{ChapSemiSup05,
  author = {O. Chapelle and A. Zien},
  title = {Semi-Supervised Classification by Low Density Separation},
  booktitle = {AI \& Statistics},
  year = {2005}
}

@CONFERENCE{Chen99GRNNPCompleteness,
  author = {T. Chen and V. Filkov and S. Skiena},
  title = {Identifying gene regulatory networks from experimental data},
  booktitle = {Proc. 3rd Ann. Int. Conf. on Comp. Mol. Biology},
  year = {1999},
  publisher = {ACM-SIGACT}
}

@ARTICLE{ChenPharmacophoreIdent99,
  author = {X. Chen and A. Rusinko and A. Tropsha and S. S. Young},
  title = {{A}utomated {P}harmacophore {I}dentification for {L}arge {C}hemical
	{D}ata {S}ets},
  journal = {J. Chem. Inf. Comput. Sci.},
  year = {1999},
  volume = {39},
  pages = {887--896}
}

@ARTICLE{cherkasskySVRModSel,
  author = {V. Cherkassky and Y. Ma},
  title = {Practical Selection of SVM Parameters and Noise Estimation for SVM
	Regression},
  journal = {Neural Networks},
  year = {2004},
  volume = {17},
  pages = {113 - 126},
  number = {1}
}

@ARTICLE{Chewawiwat1999,
  author = {N. Chewawiwat and M. Yano and K. Terada and N. J. Hoogenraad and
	M. Mori},
  title = {Characterization of the novel mitochondrial protein import component,
	Tom34, in mammalian cells.},
  journal = {J Biochem},
  year = {1999},
  volume = {125},
  pages = {721--727},
  number = {4},
  month = {Apr},
  abstract = {Tom34 is a newly-found component of the mitochondrial protein import
	machinery in mammalian cells with no apparent counterpart in fungi.
	RNA blot and immunoblot analyses showed that the expression of Tom34
	varies among tissues and differs from that of the core translocase
	component Tom20. In contrast to a previous report [Nuttal, S.D. et
	al. (1997) DNA Cell Biol. 16, 1067-1074], the present study using
	a newly-prepared anti-Tom34 antibody with a high titer showed that
	Tom34 is present largely in the cytosolic fraction and partly in
	the mitochondrial and membrane fractions after fractionation of tissues
	and cells, and that the membrane-associated form is largely extractable
	with 0.1 M sodium carbonate. The in vitro import of preproteins into
	isolated rat mitochondria was strongly inhibited by DeltahTom34 which
	lacks the NH2-terminal hydrophobic region of human Tom34 (hTom34).
	Import was also strongly inhibited by anti-hTom34. In pulse-chase
	experiments using COS-7 cells, pre-ornithine transcarbamylase (pOTC)
	was rapidly processed to the mature form. Coexpression of hTom34
	resulted in a stimulation of pOTC processing, whereas the coexpression
	of hTom34 antisense RNA caused inhibition. The results confirm that
	Tom34 plays a role in mitochondrial protein import in mammals, and
	suggest it to be an ancillary component of the translocation machinery
	in mammalian cells.},
  keywords = {Animals; Base Sequence; COS Cells; Carrier Proteins; Cytosol; DNA
	Primers; Enzyme Precursors; Female; Hela Cells; Humans; Male; Membrane
	Proteins; Membrane Transport Proteins; Membranes; Mitochondria; Mitochondrial
	Membrane Transport Proteins; Ornithine Carbamoyltransferase; Pregnancy;
	Protein Processing, Post-Translational; RNA, Messenger; Rats; Receptors,
	Cell Surface; Recombinant Proteins; Swine; Tissue Distribution},
  owner = {froehlih},
  pmid = {10101285},
  timestamp = {2008.04.02}
}

@ARTICLE{cho01,
  author = {R. Cho and M. Huang and M. Campbell and H. Dong and L. Steinmetz
	and L. Sapinoso and G. Hampton and S. Elledge and R. Davis and D.
	Lockhart},
  title = {Transcriptional regulation and function during the human cell cycle},
  journal = {Nature Genetics},
  year = {2001},
  volume = {27},
  pages = {48-54},
  number = {1},
  groupsearch = {0},
  keywords = {biological datasets}
}

@ARTICLE{cho98,
  author = {Cho, R.J. and Campbell M.J. and Winzeler, E.A. and Steinmetz, L.
	and Conway, A. and Wodicka, L. and Wolfsberg, T.G. and Gabrielian,
	A.E. and Landsman, D. and Lockhart, D.J. and Davis, R.W.},
  title = {A Genome wide transcriptional analysis of the mititic cell cycle},
  journal = {Molecular Cell},
  year = {1998},
  volume = {2},
  pages = {65-73},
  groupsearch = {0},
  keywords = {original papers on biological datasets}
}

@ARTICLE{ChowLiuMWST68,
  author = {C. Chow and C. Liu},
  title = {Approximating discrete probability distribitions with dependence
	trees},
  journal = {IEEE Trans. Inf. Theory},
  year = {1968},
  volume = {14},
  pages = {462 - 467},
  number = {3}
}

@ARTICLE{chu98,
  author = {Chu, S. and DeRisi J. and Eisen, M. and Mullholland, J. and Botstein,
	D. and Brown, P.O. and Herskowitz, I.},
  title = {The transcriptional program of sporulation in budding yeast},
  journal = {Science},
  year = {1998},
  volume = {282},
  pages = {699-705},
  groupsearch = {0},
  keywords = {original papers on biological datasets}
}

@BOOK{ChungSpectralGraph97,
  title = {Spectral Graph Theory},
  publisher = {American Mathematical Society},
  year = {1997},
  author = {F. Chung},
  number = {92},
  series = {CBMS Regional Conference Series in Mathematics},
  address = {Providence, RI}
}

@ARTICLE{ParamselSvm03,
  author = {K. Chung and W. Kao and C. Sun and L. Wang and C. Lin},
  title = {Radius Margin Bounds for Support Vector Machines with the RBF Kernel},
  journal = {Neural Computation},
  year = {2003},
  volume = {15},
  pages = {2643 - 2681},
  number = {11}
}

@ARTICLE{Cobleigh1999,
  author = {M. A. Cobleigh and C. L. Vogel and D. Tripathy and N. J. Robert and
	S. Scholl and L. Fehrenbacher and J. M. Wolter and V. Paton and S.
	Shak and G. Lieberman and D. J. Slamon},
  title = {Multinational study of the efficacy and safety of humanized anti-HER2
	monoclonal antibody in women who have HER2-overexpressing metastatic
	breast cancer that has progressed after chemotherapy for metastatic
	disease.},
  journal = {J Clin Oncol},
  year = {1999},
  volume = {17},
  pages = {2639--2648},
  number = {9},
  month = {Sep},
  abstract = {PURPOSE: Overexpression of the HER2 protein occurs in 25\% to 30\%
	of human breast cancers and leads to a particularly aggressive form
	of the disease. Efficacy and safety of recombinant humanized anti-HER2
	monoclonal antibody as a single agent was evaluated in women with
	HER2-overexpressing metastatic breast cancer that had progressed
	after chemotherapy for metastatic disease. PATIENTS AND METHODS:
	Two hundred twenty-two women, with HER2-overexpressing metastatic
	breast cancer that had progressed after one or two chemotherapy regimens,
	were enrolled. Patients received a loading dose of 4 mg/kg intravenously,
	followed by a 2-mg/kg maintenance dose at weekly intervals. RESULTS:
	Study patients had advanced metastatic disease and had received extensive
	prior therapy. A blinded, independent response evaluation committee
	identified eight complete and 26 partial responses, for an objective
	response rate of 15\% in the intent-to-treat population (95\% confidence
	interval, 11\% to 21\%). The median duration of response was 9.1
	months; the median duration of survival was 13 months. The most common
	adverse events, which occurred in approximately 40\% of patients,
	were infusion-associated fever and/or chills that usually occurred
	only during the first infusion, and were of mild to moderate severity.
	These symptoms were treated successfully with acetaminophen and/or
	diphenhydramine. The most clinically significant adverse event was
	cardiac dysfunction, which occurred in 4.7\% of patients. Only 1\%
	of patients discontinued the study because of treatment-related adverse
	events. CONCLUSION: Recombinant humanized anti-HER2 monoclonal antibody,
	administered as a single agent, produces durable objective responses
	and is well tolerated by women with HER2-overexpressing metastatic
	breast cancer that has progressed after chemotherapy for metastatic
	disease. Side effects that are commonly observed with chemotherapy,
	such as alopecia, mucositis, and neutropenia, are rarely seen.},
  keywords = {Adult; Aged; Aged, 80 and over; Antibodies, Monoclonal; Breast Neoplasms;
	Confidence Intervals; Disease Progression; Disease-Free Survival;
	Female; Heart Diseases; Humans; Middle Aged; Multivariate Analysis;
	Quality of Life; Receptor, erbB-2; Time Factors},
  owner = {froehlih},
  pmid = {10561337},
  timestamp = {2008.10.23}
}

@INPROCEEDINGS{Cohn95ActiveLearning,
  author = {David A. Cohn and Zoubin Ghahramani and Michael I. Jordan},
  title = {Active Learning with Statistical Models},
  booktitle = {Advances in Neural Information Processing Systems},
  year = {1995},
  editor = {G. Tesauro and D. Touretzky and T. Leen},
  volume = {7},
  pages = {705--712},
  publisher = {The {MIT} Press},
  url = {citeseer.ist.psu.edu/article/cohn96active.html}
}

@ARTICLE{coller00,
  author = {Coller, H.A. and Grandori, C. and Tamayo, P. and Colbert, T. and
	Lande, E.S. and Eisenman, R.N. and Golub T.R.},
  title = {Expression analysis with oligonucleotide microarrays reveals that
	MYC regulates genes involved in growth, cell cycle, signaling, and
	adhesion},
  journal = {PNAS},
  year = {2000},
  volume = {97},
  pages = {3260-3265},
  groupsearch = {0},
  keywords = {supervised methods}
}

@ARTICLE{Consortium2004NumOfGenes,
  author = {International Human Genome Sequencing Consortium},
  title = {Finishing the euchromatic sequence of the human genome.},
  journal = {Nature},
  year = {2004},
  volume = {431},
  pages = {931--945},
  number = {7011},
  month = {Oct},
  abstract = {The sequence of the human genome encodes the genetic instructions
	for human physiology, as well as rich information about human evolution.
	In 2001, the International Human Genome Sequencing Consortium reported
	a draft sequence of the euchromatic portion of the human genome.
	Since then, the international collaboration has worked to convert
	this draft into a genome sequence with high accuracy and nearly complete
	coverage. Here, we report the result of this finishing process. The
	current genome sequence (Build 35) contains 2.85 billion nucleotides
	interrupted by only 341 gaps. It covers approximately 99\% of the
	euchromatic genome and is accurate to an error rate of approximately
	1 event per 100,000 bases. Many of the remaining euchromatic gaps
	are associated with segmental duplications and will require focused
	work with new methods. The near-complete sequence, the first for
	a vertebrate, greatly improves the precision of biological analyses
	of the human genome including studies of gene number, birth and death.
	Notably, the human genome seems to encode only 20,000-25,000 protein-coding
	genes. The genome sequence reported here should serve as a firm foundation
	for biomedical research in the decades ahead.},
  keywords = {Amino Acid Sequence; Base Sequence; Centromere; Chromosomes, Artificial,
	Bacterial; Chromosomes, Human; DNA, Complementary; Euchromatin; Gene
	Duplication; Genes; Genome, Human; Heterochromatin; Human Genome
	Project; Humans; Molecular Sequence Data; Multigene Family; Physical
	Chromosome Mapping; Plasmids; Pseudogenes; Research Design; Sensitivity
	and Specificity; Sequence Analysis, DNA; Telomere},
  owner = {froehlih},
  pmid = {15496913},
  timestamp = {2008.04.22}
}

@BOOK{cormen01,
  title = {Introduction to Algorithms},
  publisher = {The MIT Press},
  year = {2001},
  author = {Cormen, T.H. and Leiserson, C.E. and Rivest, R.L. and Stein, C.},
  address = {Cambridge, Massachusetts},
  edition = {2nd},
  groupsearch = {0},
  keywords = {algorithmic books}
}

@ARTICLE{CorVap95,
  author = {C. Cortes and V. Vapnik},
  title = {Support vector networks},
  journal = {Machine Learning},
  year = {1995},
  volume = {20},
  pages = {273 - 297}
}

@INPROCEEDINGS{cotta03,
  author = {Cotta,C},
  title = {A study of Allelic Recombination},
  booktitle = {Proceedings of the 2003 Congress on Evolutionary Computation (CEC
	2003) },
  year = {2003},
  volume = {2},
  pages = {1406-1413},
  publisher = {{IEEE} Press},
  groupsearch = {0}
}

@CONFERENCE{Couto2005GraSM,
  author = {F. Couto and M. Silva and P. Coutinho},
  title = {{Semantic Similarity over the Gene Ontology: Family Correlation and
	Selecting Disjunctive Ancestors}},
  booktitle = {Conference in Information and Knowledge Management},
  year = {2005},
  owner = {froehlih},
  timestamp = {2006.08.17}
}

@TECHREPORT{Couto2003FuSSiMeg,
  author = {F. Couto and M. Silva and P. Coutinho},
  title = {{Implementation of a Functional Semantic Similarity Measure between
	Gene-Products}},
  institution = {Department of Informatics, University of Lisbon},
  year = {2003},
  number = {DI/FCUL TR 03--29},
  owner = {froehlih},
  timestamp = {2006.08.17}
}

@ARTICLE{CraCoMFA88,
  author = {R. Cramer and D. Patterson and J. Bunce},
  title = {Comparative molecular field analysis (CoMFA). 1. Effect of shape
	on binding of steroids to carrier proteins},
  journal = {J. Am. Chem. Soc.},
  year = {1988},
  volume = {110},
  pages = {5959 - 5967}
}

@BOOK{CrisSha00,
  title = {An Introduction to Support Vector Machines},
  publisher = {Cambridge University Press},
  year = {2000},
  author = {N. Cristianini and J. Shawe-Taylor}
}

@CONFERENCE{Cris02,
  author = {N. Cristianini and J. Shawn-Taylor and A. Elisseeff and J. Kandola},
  title = {On kernel-target alignment},
  booktitle = {Adv. Neural Inf. Proc. Syst. 14},
  year = {2002}
}

@ARTICLE{Cryan2000FST,
  author = {Cryan, J. and Lucki, I.},
  title = {Antidepressant-like effects mediated by 5-hydroxytryptamine2c receptors},
  journal = {J. Pharmacol. Exp. Ther.},
  year = {2000},
  volume = {295},
  pages = {1120 - 1126},
  number = {3},
  owner = {froehlih},
  timestamp = {2006.08.15}
}

@ARTICLE{Cryan2002FST,
  author = {Cryan, J.F. and Markou, A. and Lucki, I.},
  title = {Assessing antidepressant activity in rodents: recent developments
	and future needs},
  journal = {Trends. Pharmacol. Sci.},
  year = {2002},
  volume = {23},
  pages = {238-245},
  number = {5},
  owner = {froehlih},
  timestamp = {2006.07.13}
}

@ARTICLE{CsatoSparseGP02,
  author = {L. Csato and M. Opper},
  title = {Sparse Online Gaussian Processes},
  journal = {Neural Computation},
  year = {2002},
  volume = {14},
  pages = {641 - 669},
  number = {3}
}

@BOOK{R:Dalgaard:2008,
  title = {Introductory Statistics with {R}},
  publisher = {Springer},
  year = {2008},
  author = {Peter Dalgaard},
  pages = {380},
  edition = {2nd},
  note = {ISBN 978-0-387-79053-4},
  abstract = {This book provides an elementary-level introduction to R, targeting
	both non-statistician scientists in various fields and students of
	statistics. The main mode of presentation is via code examples with
	liberal commenting of the code and the output, from the computational
	as well as the statistical viewpoint. A supplementary R package can
	be downloaded and contains the data sets. The statistical methodology
	includes statistical standard distributions, one- and two-sample
	tests with continuous data, regression analysis, one- and two-way
	analysis of variance, regression analysis, analysis of tabular data,
	and sample size calculations. In addition, the last six chapters
	contain introductions to multiple linear regression analysis, linear
	models in general, logistic regression, survival analysis, Poisson
	regression, and nonlinear regression.},
  orderinfo = {springer.txt},
  publisherurl = {http://www.springer.com/statistics/computational/book/978-0-387-79053-4},
  url = {http://www.biostat.ku.dk/~pd/ISwR.html}
}

@ARTICLE{davies79,
  author = {Davies, J.L. and Bouldin, D.W.},
  title = {A cluster separation measure},
  journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
  year = {1979},
  volume = {1},
  pages = {224-227},
  groupsearch = {0},
  keywords = {mathematical clustering}
}

@CONFERENCE{DavRus94,
  author = {S. Davies and S. Russel},
  title = {{NP-Completeness of Searches for Smallest Possible Feature Sets}},
  booktitle = {Proc. 1994 AAAI Fall Symposion on Relevance},
  year = {1994},
  pages = {37 - 39}
}

@BOOK{dawkins76,
  title = {The selfish Gene},
  publisher = {Oxford University Press},
  year = {1976},
  author = {Dawkins, R.},
  groupsearch = {0},
  keywords = {evolutionary algorithms}
}

@ARTICLE{DempsterEM77,
  author = {A. Dempster and N. Laird and D. Rubin},
  title = {Maximum likelihood from incomplete data via the EM algorithm},
  journal = {J. Royal Statistical Soc., Series B},
  year = {1977},
  volume = {39},
  pages = {1 - 38},
  number = {1}
}

@CONFERENCE{DennisPatternSearch94,
  author = {J. Dennis and V. Torczon},
  title = {Derivative-free pattern search methods for multidisciplinary design
	problems},
  booktitle = {Proc. 5th AIAA/USAF/NASA/ISSMO Symposium on Multidisciplinary Analysis
	and Optimization},
  year = {1994},
  pages = {922 - 932}
}

@ARTICLE{Detke1995FST,
  author = {Detke, M.J. and Rickels, M. and Licki, I.},
  title = {Active behaviors in the rat forced swimming test differently produced
	by serotonergic and noradrenergic antidepressants},
  journal = {Psychopharmacology},
  year = {1995},
  volume = {121},
  pages = {66-72},
  owner = {froehlih},
  timestamp = {2006.07.13}
}

@ARTICLE{Dietterich1998StatisticalTests,
  author = {T. Dietterich},
  title = {Approximate Statistical Tests for Comparing Supervised Classification
	Learning Algorithms},
  journal = {Neural Computation},
  year = {1998},
  volume = {10},
  pages = {1895 - 1924},
  owner = {froehlih},
  timestamp = {2007.08.22}
}

@CONFERENCE{DingPCAKmeans,
  author = {C. Ding and X. He},
  title = {K-means Clustering via Principal Component Analysis},
  booktitle = {Proc. Int. Conf. on Machine Learning},
  year = {2004},
  pages = {225 - 232}
}

@CONFERENCE{DingMinMaxCut01,
  author = {C. Ding and X. He and H. Zha and M. Gu and H. Simon},
  title = {Spectral Min-Max Cut for Graph Partitioning and Data Clustering},
  booktitle = {Proc. 1st IEEE Int. Conf. Data Mining},
  year = {2001},
  pages = {107 -114}
}

@ARTICLE{doniger03,
  author = {S.W. Doniger and N.Salomonis and K.D. Dahlqusi and K. Vranizan and
	S.C. Lawlor and B.R. Conklin},
  title = {{MAPPF}inder: using {G}ene {O}ntology and {G}en{MAPP} to create a
	global gene-expression profile from microarray data},
  journal = {Genome Biology},
  year = {2003},
  volume = {4},
  pages = {R7},
  number = {1}
}

@ARTICLE{Driessche2005Epistasis,
  author = {N. Van Driessche and J. Demsar and E. Booth and P. Hill and P. Juvan
	and B. Zupan and A. Kuspa and G. Shaulsky},
  title = {Epistasis Analysis with Global Transcriptional Phenotypes},
  journal = {Nature Genetics},
  year = {2005},
  volume = {37},
  pages = {471 - 477},
  number = {5},
  owner = {froehlih},
  timestamp = {2007.09.17}
}

@BOOK{DudaHart2001,
  title = {Pattern Classification},
  publisher = {Wiley-Interscience},
  year = {2001},
  author = {R. Duda and P. Hart and D. Stork},
  address = {New York}
}

@ARTICLE{dunn74,
  author = {Dunn, J.C.},
  title = {Well separated clusters and optimal fuzzy partitions},
  journal = {Journal of Cybernetics},
  year = {1974},
  volume = {4},
  pages = {95-104}
}

@ARTICLE{Efron2002localFDR,
  author = {B. Efron and R. Tibshirani},
  title = {Empirical Bayes methods and false discovery rates for microarrays},
  journal = {Genetic Epidemiology},
  year = {2002},
  volume = {23},
  pages = {70 - 86},
  owner = {froehlih},
  timestamp = {2007.02.02}
}

@INPROCEEDINGS{eisen98,
  author = {M. Eisen and P. Spellman and D. Botstein and P. Brown},
  title = {Cluster Analysis and Display of Genome-wide Expression Patterns.},
  booktitle = {Proceedings of the National Academy of Sciences, USA},
  year = {1998},
  volume = {95},
  pages = {14863-14867},
  groupsearch = {0},
  keywords = {mathematical clustering}
}

@BOOK{Eshelman91,
  title = {The CHC Adaptive Search Algorithm, How to Have a Safe Search When
	Engaging in Non-traditional Genetic Recombination},
  publisher = {Morgan Kaufman},
  year = {1991},
  author = {L. Eshelman},
  pages = {265 - 283}
}

@ARTICLE{feherBBB00,
  author = {M. Feher and E. Sourial and J. Schmidt},
  title = {A simple model for the prediction of blood-brain partitioning},
  journal = {Int. J. Pharmaceut.},
  year = {2000},
  volume = {201},
  pages = {239 - 247}
}

@BOOK{fellbaum98,
  title = {{WordNet. An electronic lexical database}},
  publisher = {MIT Press},
  year = {1998},
  author = {Fellbaum, C.},
  address = {Massachusetts, Cambidge},
  groupsearch = {0},
  keywords = {semantic distances}
}

@ARTICLE{FigueareasRingPercept96,
  author = {J. Figueras},
  title = {{R}ing {P}erception {U}sing {B}readth--{F}irst {S}earch},
  journal = {J. Chem. Inf. Comput. Sci.},
  year = {1996},
  volume = {36},
  pages = {986--991},
  abstract = {Combining breadth-first search with new ideas for uncovering embedded
	rings in complex systems 1 yields a very fast routine for ring perception.
	With large structures, the new routine is orders of magnitude faster
	than depth-first ring detection, a result expected on the basis of
	recent work that establishes polynomial order for BFS.2},
  contents = {Smallest Set of Smallest Ring (SSSR), Bread First Search (BFS), Binary
	Edge Encoded Path (BEEP), message passing algorithm}
}

@ARTICLE{FireMelloRNAinterference1998,
  author = {Fire, A. and Xu, S. and Montgomery, M.K. and Kostas, S.A. and Driver,
	S.E. and Mello, C.C.},
  title = {Potent and specific genetic interference by double-stranded RNA in
	Caenorhabditis elegans.},
  journal = {Nature},
  year = {1998},
  volume = {391},
  pages = {806 - 811},
  owner = {froehlih},
  timestamp = {2007.05.02}
}

@ARTICLE{Flicek2008Ensembl,
  author = {P. Flicek and B. L. Aken and K. Beal and B. Ballester and M. Caccamo
	and Y. Chen and L. Clarke and G. Coates and F. Cunningham and T.
	Cutts and T. Down and S. C. Dyer and T. Eyre and S. Fitzgerald and
	J. Fernandez-Banet and S. Gr�f and S. Haider and M. Hammond and
	R. Holland and K. L. Howe and K. Howe and N. Johnson and A. Jenkinson
	and A. K�h�ri and D. Keefe and F. Kokocinski and E. Kulesha and
	D. Lawson and I. Longden and K. Megy and P. Meidl and B. Overduin
	and A. Parker and B. Pritchard and A. Prlic and S. Rice and D. Rios
	and M. Schuster and I. Sealy and G. Slater and D. Smedley and G.
	Spudich and S. Trevanion and A. J. Vilella and J. Vogel and S. White
	and M. Wood and E. Birney and T. Cox and V. Curwen and R. Durbin
	and X. M. Fernandez-Suarez and J. Herrero and T. J P Hubbard and
	A. Kasprzyk and G. Proctor and J. Smith and A. Ureta-Vidal and S.
	Searle},
  title = {Ensembl 2008},
  journal = {Nucleic Acids Res},
  year = {2008},
  volume = {36},
  pages = {D707--D714},
  number = {Database issue},
  month = {Jan},
  doi = {10.1093/nar/gkm988},
  keywords = {Animals; Computer Graphics; Databases, Genetic; Genomics; Humans;
	Internet; Mice; Regulatory Elements, Transcriptional; Software; User-Computer
	Interface},
  owner = {froehlih},
  pii = {gkm988},
  pmid = {18000006},
  timestamp = {2008.04.01},
  url = {http://dx.doi.org/10.1093/nar/gkm988}
}

@MASTERSTHESIS{Froe02,
  author = {H. Fr\"ohlich},
  title = {{Feature Selection for Support Vector Machines by Means of Genetic
	Algorithms}},
  school = {University of Marburg},
  year = {2002},
  note = {http://www-ra/informatik.uni-tuebingen.de/mitarb/froehlich}
}

@ARTICLE{Froehlich2008,
  author = {H. Fr\"ohlich and T. Bei{\ss}barth and A. Tresch and D. Kostka and
	J. Jacob and R. Spang and F. Markowetz},
  title = {{Analyzing gene perturbation screens with nested effects models in
	R and bioconductor}},
  journal = {Bioinformatics},
  year = {2008},
  volume = {24},
  pages = {2549--2550},
  number = {21},
  month = {Nov},
  abstract = {Nested effects models (NEMs) are a class of probabilistic models introduced
	to analyze the effects of gene perturbation screens visible in high-dimensional
	phenotypes like microarrays or cell morphology. NEMs reverse engineer
	upstream/downstream relations of cellular signaling cascades. NEMs
	take as input a set of candidate pathway genes and phenotypic profiles
	of perturbing these genes. NEMs return a pathway structure explaining
	the observed perturbation effects. Here, we describe the package
	nem, an open-source software to efficiently infer NEMs from data.
	Our software implements several search algorithms for model fitting
	and is applicable to a wide range of different data types and representations.
	The methods we present summarize the current state-of-the-art in
	NEMs. AVAILABILITY: Our software is written in the R language and
	freely avail-able via the Bioconductor project at http://www.bioconductor.org.},
  doi = {10.1093/bioinformatics/btn446},
  institution = {German Cancer Research Center, INF 580, 69120 Heidelberg, Germany.},
  keywords = {Algorithms; Gene Expression; Gene Expression Profiling; Models, Statistical;
	Oligonucleotide Array Sequence Analysis; Software; User-Computer
	Interface},
  owner = {froehlih},
  pii = {btn446},
  pmid = {18718939},
  timestamp = {2008.11.25},
  url = {http://dx.doi.org/10.1093/bioinformatics/btn446}
}

@ARTICLE{FroeChapScho04FeatselGA,
  author = {H. Fr\"ohlich and O. Chapelle and B. Sch\"olkopf},
  title = {{Feature Selection for Support Vector Machines using Genetic Algorithms}},
  journal = {Int. J. AI Tools: Special Issue on Selected Papers from the 15th
	IEEE Int. Conf. on Tools with AI 2003},
  year = {2004},
  volume = {13},
  pages = {791 - 800},
  number = {4}
}

@CONFERENCE{Froe03,
  author = {H. Fr\"ohlich and O. Chapelle and B. Sch\"olkopf},
  title = {{Feature Selection for Support Vector Machines by Means of Genetic
	Algorithms}},
  booktitle = {Proc. 15th IEEE Int. Conf. on Tools with AI},
  year = {2003},
  pages = {142 - 148}
}

@ARTICLE{Frohlich2008NEMsBioinformatics,
  author = {H. Fr\"ohlich and M. Fellmann and H. S\"ultmann and A. Poustka and
	T. Bei{\ss}barth},
  title = {{Estimating Large Scale Signaling Networks through Nested Effect
	Models with Intervention Effects from Microarray Data}},
  journal = {Bioinformatics},
  year = {2008},
  volume = {24},
  pages = {2650-2656},
  note = {doi: 10.1093/bioinformatics/btm634},
  owner = {froehlih},
  timestamp = {2008.01.10}
}

@CONFERENCE{Frohlich2007GCBRNAi,
  author = {H. Fr\"ohlich and M. Fellmann and H. S\"ultmann and A. Poustka and
	T. Bei{\ss}barth},
  title = {Estimating Large Scale Signaling Networks through Nested Effects
	Models from Intervention Effects in Microarray Data},
  booktitle = {Proc. German Conf. on Bioinformatics},
  year = {2007},
  pages = {45 - 54},
  owner = {froehlih},
  timestamp = {2007.09.14}
}

@ARTICLE{Frohlich2007RNAiBMC,
  author = {H. Fr\"ohlich and M. Fellmann and H. S\"ultmann and A. Poustka and
	T. Bei{\ss}barth},
  title = {{Large Scale Statistical Inference of Signaling Pathways from RNAi
	and Microarray Data}},
  journal = {BMC Bioinformatics},
  year = {2007},
  volume = {8},
  pages = {386},
  owner = {froehlih},
  timestamp = {2007.10.01}
}

@CONFERENCE{FroeSpikes05,
  author = {H. Fr\"ohlich and B. Naundorf and M. Volgushev and F. Wolf},
  title = {Which Features Trigger Action Potentials in Cortical Neurons in Vivo?},
  booktitle = {Proc. Int. Joint Conf. Neural Networks},
  year = {2005},
  pages = {250 - 255}
}

@ARTICLE{Frohlich2009DEPNs,
  author = {H. Fr\"ohlich and \"O. Sahin and D. Arlt and C. Bender and T. Beissbarth},
  title = {{Deterministic Effects Propagation Networks for Reconstructing Protein
	Signaling Networks from Multiple Interventions}},
  journal = {BMC Bioinformatics},
  year = {2009},
  volume = {10},
  pages = {322},
  owner = {holfro},
  timestamp = {2009.05.14}
}

@ARTICLE{Froehlich2007GOSim,
  author = {H. Fr\"ohlich and N. Speer and A. Poustka and T. Beissbarth},
  title = {{GOSim - An R-Package for Computation of Information Theoretic GO
	Similarities Between Terms and Gene Products}},
  journal = {BMC Bioinformatics},
  year = {2007},
  volume = {8},
  pages = {166},
  owner = {froehlih},
  timestamp = {2007.12.07}
}

@CONFERENCE{FroeSpeerGOKer06,
  author = {H. Fr\"ohlich and N. Speer and A. Zell},
  title = {Kernel Based Functional Gene Grouping},
  booktitle = {Proc. Int. Joint Conf. Neural Networks},
  year = {2006},
  pages = {6886 - 6891}
}

@ARTICLE{Frohlich2009NEMComplete,
  author = {H. Fr\"ohlich and A. Tresch and T. Beissbarth},
  title = {Nested Effects Models for Learning Signaling Networks from Perturbation
	Data},
  journal = {Biometrical Journal},
  year = {2009},
  volume = {2},
  pages = {304 - 323},
  number = {51},
  owner = {holfro},
  timestamp = {2009.07.27}
}

@CONFERENCE{FroehOAKernelsICML05,
  author = {H. Fr\"ohlich and J. Wegner and F. Sieker and A. Zell},
  title = {Optimal Assignment Kernels For Attributed Molecular Graphs},
  booktitle = {Proc. Int. Conf. Machine Learning},
  year = {2005},
  editor = {L. De Raedt and S. Wrobel},
  pages = {225 - 232},
  publisher = {ACM Press}
}

@ARTICLE{FroehOAKernelsQSAR05,
  author = {H. Fr\"ohlich and J. Wegner and F. Sieker and A. Zell},
  title = {Kernel Functions for Attributed Molecular Graphs -- A New Similarity
	Based Approach To ADME Prediction in Classification and Regression},
  journal = {QSAR \& Comb. Sci.},
  year = {2006},
  volume = {25},
  pages = {317 - 326},
  number = {4},
  publisher = {Wiley Interscience}
}

@CONFERENCE{FroehOAKernelsIJCNN05,
  author = {H. Fr\"ohlich and J. Wegner and A. Zell},
  title = {Assignment Kernels For Chemical Compounds},
  booktitle = {Proc. Int. Joint Conf. Neural Networks},
  year = {2005},
  pages = {913 - 918}
}

@ARTICLE{FroeQSAR04,
  author = {H. Fr\"ohlich and J. K. Wegner and A. Zell},
  title = {{T}owards {O}ptimal {D}escriptor {S}ubset {S}election with {S}upport
	{V}ector {M}achines in {C}lassification and {R}egression},
  journal = {QSAR \& Comb. Sci.},
  year = {2004},
  volume = {23},
  pages = {311--318},
  abstract = {In this paper we present a novel method for selecting descriptor subsets
	by means of Support Vector Machines in classification and regression
	- the Incremental Regularized Risk Minimization (IRRM) algorithm.
	In contrast to many other wrapper methods it is fully deterministic
	and computationally efficient. We compare our method to existing
	algorithms and present results on a Human Intestinal Absorption (HIA)
	classification data set and the Huuskonen regression data set for
	aqueous solubility.}
}

@CONFERENCE{FroehEPSGO05,
  author = {H. Fr\"ohlich and A. Zell},
  title = {Efficient Parameter Selection for Support Vector Machines in Classification
	and Regression via Model-Based Global Optimization},
  booktitle = {Proc. Int. Joint Conf. Neural Networks},
  year = {2005},
  pages = {1431 - 1438}
}

@CONFERENCE{Froe04,
  author = {H. Fr\"ohlich and A. Zell},
  title = {{Feature Subset Selection for Support Vector Machines by Incremental
	Regularized Risk Minimization}},
  booktitle = {Proc. IEEE Int. Joint Conf. on Neural Networks (IJCNN)},
  year = {2004},
  volume = {3},
  pages = {2041 - 2046}
}

@ARTICLE{Fraley2002Mclust,
  author = {C. Fraley and A. E. Raftery},
  title = {Model-based clustering, discriminant analysis, and density estimation},
  journal = {Journal of the American Statistical Association},
  year = {2002},
  volume = {97},
  pages = {611:631},
  owner = {holfro},
  timestamp = {2009.05.06}
}

@ARTICLE{FreundBoosting1996,
  author = {Y. Freund and R. Shapire},
  title = {A decision-theoretic generalization of on-line learning and an application
	to boosting},
  journal = {J. Comp. and Syst. Sci.},
  year = {1996},
  volume = {55},
  pages = {119 - 139},
  number = {1},
  owner = {froehlih},
  timestamp = {2006.10.26}
}

@ARTICLE{Fried05SVDimpute,
  author = {S. Friedland and A. Niknejad and L. Chihara},
  title = {{A Simultaneous Reconstruction of Missing Data in DNA Microarrays}},
  journal = {Linear Algebra Appl.},
  year = {2005},
  note = {to appear}
}

@ARTICLE{Froehlich2011,
  author = {Holger Fr�hlich and Paurush Praveen and Achim Tresch},
  title = {{Fast and efficient dynamic nested effects models.}},
  journal = {Bioinformatics},
  year = {2011},
  volume = {27},
  pages = {238--244},
  number = {2},
  month = {Jan},
  abstract = {Targeted interventions in combination with the measurement of secondary
	effects can be used to computationally reverse engineer features
	of upstream non-transcriptional signaling cascades. Nested effect
	models (NEMs) have been introduced as a statistical approach to estimate
	the upstream signal flow from downstream nested subset structure
	of perturbation effects. The method was substantially extended later
	on by several authors and successfully applied to various datasets.
	The connection of NEMs to Bayesian Networks and factor graph models
	has been highlighted.Here, we introduce a computationally attractive
	extension of NEMs that enables the analysis of perturbation time
	series data, hence allowing to discriminate between direct and indirect
	signaling and to resolve feedback loops.The implementation (R and
	C) is part of the Supplement to this article.},
  doi = {10.1093/bioinformatics/btq631},
  institution = {Rheinische Friedrich-Wilhelms-Universit�t Bonn, Bonn-Aachen International
	Center for IT, Bonn, Germany. frohlich@bit.uni-bonn.de},
  keywords = {Animals; Computer Simulation; Embryonic Stem Cells; Gene Regulatory
	Networks; Mice; Models, Biological; Models, Statistical; Signal Transduction},
  owner = {frohlich},
  pii = {btq631},
  pmid = {21068003},
  timestamp = {2012.01.30},
  url = {http://dx.doi.org/10.1093/bioinformatics/btq631}
}

@ARTICLE{GasteigerPartialCharge78,
  author = {J. Gast\-eiger and M. Marsili},
  title = {{A} {N}ew {M}odel for {C}alculating {A}tomic {C}harges in {M}olecules},
  journal = {Tetrahedron Lett.},
  year = {1978},
  volume = {34},
  pages = {3181--3184}
}

@ARTICLE{gatViks03,
  author = {I. Gat-Viks and R. Sharan and R. Shamir},
  title = {Scoring clustering solutions by their biological relevance},
  journal = {Bioinformatics},
  year = {2003},
  volume = {19},
  pages = {2381-2389},
  number = {18},
  doi = {DOI: 10.1093/bioinformatics/btg330}
}

@ARTICLE{GatViks2006FactorGraph,
  author = {I. Gat-Viks and A. Tanay and D. Raijman and R. Shamir},
  title = {A probabilistic methodology for integrating knowledge and experiments},
  journal = {J. Comp. Biol.},
  year = {2006},
  volume = {13},
  pages = {165 - 181},
  number = {2},
  owner = {froehlih},
  timestamp = {2008.02.18}
}

@BOOK{GelmanBayesBook2004,
  title = {Bayesian Data Analysis},
  publisher = {Chapman \& Hall/CRC},
  year = {2004},
  author = {A. Gelman and J. Carlin and H. Stern and D. Rubin},
  owner = {froehlih},
  timestamp = {2006.10.23}
}

@MISC{Gene2004,
  author = {{Gene {L}ynx}},
  howpublished = {http://www.genelynx.org},
  year = {2004},
  groupsearch = {0},
  keywords = {software},
  owner = {froehlih},
  timestamp = {2008.10.02}
}

@MISC{genelynx,
  author = {{Gene {L}ynx}},
  howpublished = {http://www.genelynx.org},
  year = {2004},
  groupsearch = {0},
  keywords = {software}
}

@ARTICLE{Gille1999f,
  author = {H. Gille and J. Downward},
  title = {Multiple ras effector pathways contribute to G(1) cell cycle progression.},
  journal = {J Biol Chem},
  year = {1999},
  volume = {274},
  pages = {22033--22040},
  number = {31},
  month = {Jul},
  abstract = {The involvement of Ras in the activation of multiple early signaling
	pathways is well understood, but it is less clear how the various
	Ras effectors interact with the cell cycle machinery to cause G(1)
	progression. Ras-mediated activation of extracellular-regulated kinase/mitogen-activated
	protein kinase has been implicated in cyclin D(1) up-regulation,
	but there is little extracellular-regulated kinase activity during
	the later stages of G(1), when cyclin D(1) expression becomes maximal,
	implying that other effector pathways may also be important in cyclin
	D(1) induction. We have addressed the involvement of Ras effectors
	from the phosphatidylinositol (PI) 3-kinase and Ral-GDS families
	in G(1) progression and compared it to that of the Raf/mitogen-activated
	protein kinase pathway. PI 3-kinase activity is required for the
	expression of endogenous cyclin D(1) and for S phase entry following
	serum stimulation of quiescent NIH 3T3 fibroblasts. Activated PI
	3-kinase induces cyclin D(1) transcription and E2F activity, at least
	in part mediated by the serine/threonine kinase Akt/PKB, and to a
	lesser extent the Rho family GTPase Rac. In addition, both activated
	Ral-GDS-like factor and Raf stimulate cyclin D(1) transcription and
	E2F activity and act in synergy with PI 3-kinase. Therefore, multiple
	cooperating pathways mediate the effects of Ras on progression through
	the cell cycle.},
  keywords = {1-Phosphatidylinositol 3-Kinase; 3T3 Cells; Animals; Calcium-Calmodulin-Dependent
	Protein Kinases; Cell Cycle; Cyclin D1; G1 Phase; GTPase-Activating
	Proteins; Gene Expression Regulation; Genes, Reporter; Humans; Kinetics;
	Mice; Promoter Regions (Genetics); Protein-Serine-Threonine Kinases;
	Proteins; Proto-Oncogene Proteins; Proto-Oncogene Proteins c-akt;
	Proto-Oncogene Proteins c-raf; S Phase; Signal Transduction; Transfection;
	ras GTPase-Activating Proteins; ras Proteins},
  owner = {froehlih},
  pmid = {10419529},
  timestamp = {2008.10.23}
}

@ARTICLE{Godden2003RecPartitioning,
  author = {J. Godden and J. Furr and L. Xue and F. Stahura and J. Bajorath},
  title = {Recursive Median Partitioning for Virtual Screening of Large Databases},
  journal = {J. Chem. Inf. Comp. Sci.},
  year = {2003},
  volume = {43},
  pages = {182 - 188},
  owner = {holger},
  timestamp = {2006.10.15}
}

@ARTICLE{goeman04,
  author = {Goeman, J.J. and van de Geer, S.A. and de Kort, F. and van Houwelingen,
	H.C.},
  title = {A global test for groups of genes: testing association with a clinical
	outcome.},
  journal = {Bioinformatics},
  year = {2004},
  volume = {20},
  pages = {93-99},
  number = {1},
  doi = {10.1093/bioinformatics/btg382}
}

@CONFERENCE{GohlkeHIA01,
  author = {H. Gohlke and F. Dullweber and W. Kamm and J. M\"arz and T. Kissel
	and G. Klebe},
  title = {Prediction of Human Intestinal Absorption using a combined 'Simmulated
	Annealing/Backpropagation Neural Network' Approach},
  booktitle = {Rational Approaches Drug Des.},
  year = {2001},
  editor = {H.-D. H\"ultje and W. Sippl},
  pages = {261 - 270},
  address = {Barcelona},
  publisher = {Prous Science Press}
}

@BOOK{Goldberg98,
  title = {Genetic Algorithms in Search, Optimization and Machine Learning},
  publisher = {Addison Wesley},
  year = {1998},
  author = {D. Goldberg},
  address = {Reading}
}

@ARTICLE{Golub99,
  author = {T. Golub and D. Slonim and P. Tamayo and C. Huard and M. Gaasenbeek
	and J. Mesirov and H. Coller and M. Loh and J. Downing and M. Caligiuri
	and C. D. Bloomfield and E. S. Lander},
  title = {{Molecular classification of cancer: Class discovery and class prediction
	by gene expression monitoring}},
  journal = {Science},
  year = {1999},
  volume = {286},
  pages = {531 - 537}
}

@ARTICLE{golub99,
  author = {Golub, T.R. and Slonim, D.K. and Tamayo, P. and Huard, C. and Gaasenbeek,
	M. and Mesirov, J.P. and Coller, H. and Loh, M.L. and Downing, J.R.
	and Caliguri, M.A. and Bloomfield, C.D. and Lander, E.S.},
  title = {Molecular Classification of Cancer: Class Discovery by Gene Expression
	Monitoring},
  journal = {Science},
  year = {1999},
  volume = {286},
  pages = {531-537},
  groupsearch = {0},
  keywords = {supervised methods}
}

@ARTICLE{OrlandRGonzalez10112006SimAnn,
  author = {Gonzalez, Orland R. and Kuper, Christoph and Jung, Kirsten and Naval,
	Prospero C., Jr. and Mendoza, Eduardo},
  title = {{Parameter estimation using Simulated Annealing for S-system models
	of biochemical networks}},
  journal = {Bioinformatics},
  year = {2006},
  pages = {btl522},
  abstract = {Motivation: High-throughput technologies now allow the acquisition
	of biological data such as comprehensive biochemical time-courses
	at unprecedented rates. These temporal profiles carry topological
	and kinetic information regarding the biochemical network from which
	they were drawn. Retrieving this information will require systematic
	application of both experimental and computational methods.Results:
	S-systems are non-linear mathematical approximative models based
	on the power-law formalism. They provide a general framework for
	the simulation of integrated biological systems exhibiting complex
	dynamics such as genetic circuits, signal transduction and metabolic
	networks. We describe how the heuristic optimization technique Simulated
	Annealing can be effectively used for estimating the parameters of
	S-systems from time-course biochemical data. We demonstrate our methods
	using 3 artificial networks designed to simulate different network
	topologies and behavior. We then end with an application to a real
	biochemical network by creating a working model for the cadBA system
	in E. coli.Availability: The source code written in C++ is available
	at http://www.engg.upd.edu.ph/~naval/bioinformcode.html. All the
	necessary programs including the required compiler are described
	in a document archived with the source code.},
  doi = {10.1093/bioinformatics/btl522},
  eprint = {http://bioinformatics.oxfordjournals.org/cgi/reprint/btl522v1.pdf},
  url = {http://bioinformatics.oxfordjournals.org/cgi/content/abstract/btl522v1}
}

@ARTICLE{gowda78,
  author = {Gowda, K.C. and Krishna, G.},
  title = {Agglomertative clustering using the concept of mutual nearest neighborhood},
  journal = {Pattern Recognition},
  year = {1978},
  volume = {10},
  pages = {105-112},
  groupsearch = {0},
  keywords = {mathematical clustering}
}

@ARTICLE{GutmannRBFOpt01,
  author = {H. Gutmann},
  title = {A radial basis function method for global optimization},
  journal = {J. Global Optimization},
  year = {2001},
  volume = {19},
  pages = {201 - 227},
  number = {3}
}

@ARTICLE{GuyEli03,
  author = {I. Guyon and A. Elisseeff},
  title = {{An Introduction into Variable and Feature Selection}},
  journal = {{J. Machine Learning Research Special Issue on Variable and Feature
	Selection}},
  year = {2003},
  volume = {3},
  pages = {1157 - 1182}
}

@ARTICLE{GuyWesBarVap02,
  author = {I. Guyon and J. Weston and S. Barnhill and V. Vapnik},
  title = {{Gene Selection for Cancer Classification using Support Vector Machines}},
  journal = {Machine Learning},
  year = {2002},
  volume = {46},
  pages = {389 - 422}
}

@CONFERENCE{gaertnerGraphKer03,
  author = {T. G{\"a}rtner and P. Flach and S. Wrobel},
  title = {{On graph kernels: Hardness results and efficient alternatives}},
  booktitle = {Proc. 16th Ann. Conf. Comp. Learning Theory and 7th Ann. Workshop
	on Kernel Machines},
  year = {2003}
}

@ARTICLE{Hahne2008,
  author = {Florian Hahne and Alexander Mehrle and Dorit Arlt and Annemarie Poustka
	and Stefan Wiemann and Tim Beissbarth},
  title = {Extending pathways based on gene lists using InterPro domain signatures.},
  journal = {BMC Bioinformatics},
  year = {2008},
  volume = {9},
  pages = {3},
  abstract = {BACKGROUND: High-throughput technologies like functional screens and
	gene expression analysis produce extended lists of candidate genes.
	Gene-Set Enrichment Analysis is a commonly used and well established
	technique to test for the statistically significant over-representation
	of particular pathways. A shortcoming of this method is however,
	that most genes that are investigated in the experiments have very
	sparse functional or pathway annotation and therefore cannot be the
	target of such an analysis. The approach presented here aims to assign
	lists of genes with limited annotation to previously described functional
	gene collections or pathways. This works by comparing InterPro domain
	signatures of the candidate gene lists with domain signatures of
	gene sets derived from known classifications, e.g. KEGG pathways.
	RESULTS: In order to validate our approach, we designed a simulation
	study. Based on all pathways available in the KEGG database, we create
	test gene lists by randomly selecting pathway genes, removing these
	genes from the known pathways and adding variable amounts of noise
	in the form of genes not annotated to the pathway. We show that we
	can recover pathway memberships based on the simulated gene lists
	with high accuracy. We further demonstrate the applicability of our
	approach on a biological example. CONCLUSION: Results based on simulation
	and data analysis show that domain based pathway enrichment analysis
	is a very sensitive method to test for enrichment of pathways in
	sparsely annotated lists of genes. An R based software package domainsignatures,
	to routinely perform this analysis on the results of high-throughput
	screening, is available via Bioconductor.},
  doi = {10.1186/1471-2105-9-3},
  keywords = {Algorithms; Amino Acid Sequence; Database Management Systems; Databases,
	Protein; Gene Expression Profiling; Molecular Sequence Data; Oligonucleotide
	Array Sequence Analysis; Protein Structure, Tertiary; Proteins; Signal
	Transduction; Structure-Activity Relationship},
  owner = {froehlih},
  pii = {1471-2105-9-3},
  pmid = {18177498},
  timestamp = {2008.03.25},
  url = {http://dx.doi.org/10.1186/1471-2105-9-3}
}

@ARTICLE{hal98all,
  author = {T. A. Halgren},
  title = {{M}erck molecular force field. {I--V}. {MMFF94} {B}asics and {P}arameters},
  journal = {J. Comput. Chem.},
  year = {1998},
  volume = {17},
  pages = {490--641},
  doi = {10.1002/(SICI)1096-987X(199604)17:5/6<490::AID-JCC1>3.0.CO;2-P},
  owner = {JoergKurtWegner},
  timestamp = {2006.10.15}
}

@ARTICLE{hanisch02,
  author = {D. Hanisch and A. Zien and R. Zimmer and T. Lengauer},
  title = {Co-clustering of biological networks and gene expression data},
  journal = {Bioinformatics},
  year = {2002},
  volume = {18 (Supplement)},
  pages = {S145-S154},
  groupsearch = {0},
  keywords = {co-clustering methods}
}

@INPROCEEDINGS{harel01,
  author = {Harel, D. and Koren, Y.},
  title = {Clustering Spatial Data Using Random Walks},
  booktitle = {Proceedings of the seventh ACM SIGKDD international conference on
	Knowledge discovery and data mining},
  year = {2001},
  pages = {281-286},
  publisher = {ACM Press, New York, NY, USA},
  groupsearch = {0},
  keywords = {mathematical clustering}
}

@INPROCEEDINGS{hartemink01,
  author = {Hartemink, A.J. and Gifford, D.K. and Jaakkola, T.S. and Young, R.A.},
  title = {Using graphical models and genomic expression data to statistically
	validate models of genetic regulatory networks},
  booktitle = {Proceedings of the 2001 Pacific Symposium on Biocomputing (PSB 2001)},
  year = {2001},
  pages = {422-33.},
  groupsearch = {0},
  keywords = {gene network inference}
}

@ARTICLE{HastieSVMModeSel04,
  author = {T. Hastie and S. Rosset and R. Tishbirani and J. Zhu},
  title = {The Entire Regularization Path for Support Vector Machines},
  journal = {J. Machine Learning Research},
  year = {2004},
  volume = {5},
  pages = {1391 - 1415}
}

@BOOK{HastieTibshiraniBook2001,
  title = {The Elements of Statistical Learning},
  publisher = {Springer},
  year = {2001},
  author = {T. Hastie and R. Tibshirani and J. Friedman},
  address = {New York, NY, USA},
  owner = {froehlih},
  timestamp = {2006.10.23}
}

@TECHREPORT{Haussler99,
  author = {D. Haussler},
  title = {Convolution Kernels on Discrete Structures},
  institution = {University of California Santa Cruz},
  year = {1999},
  number = {UCSC-CRL-99-10}
}

@ARTICLE{HeckermanBN97,
  author = {D. Heckerman},
  title = {A Tutorial on Learning With Bayesian Networks},
  journal = {Data Mining and Knowledge Discovery},
  year = {1997},
  volume = {1},
  pages = {79 - 119}
}

@ARTICLE{vanHelden00,
  author = {van Helden, J. and Gilbert, D. and Wernisch, L. and Schroeder, K.
	and Wodak, S.},
  title = {Applications of regulatory sequence analysis and metabolic network
	analysis to the interpretation of gene expression data},
  journal = {LNCS},
  year = {2000},
  volume = {2066},
  pages = {155-172},
  groupsearch = {0},
  keywords = {pathway scoring methods}
}

@ARTICLE{Helma01,
  author = {C. Helma and R. King and S. Kramer},
  title = {The Predictive Toxicology Challenge 2000-2001},
  journal = {Bioinformatics},
  year = {2001},
  volume = {17},
  pages = {107 - 108}
}

@ARTICLE{Hoeffding63,
  author = {W. Hoeffding},
  title = {Probability inequalities for sums of bounded random variables},
  journal = {J. American Statist. Assoc.},
  year = {1963},
  volume = {58},
  pages = {13 - 30}
}

@ARTICLE{Hoerl70RidgeReg,
  author = {A. Hoerl and R. Kennard},
  title = {Ridge regression: Biased estimation for nonorthogonal problems},
  journal = {Technometrics},
  year = {1970},
  volume = {12},
  pages = {55 - 67},
  number = {1}
}

@ARTICLE{HotellingCCA36,
  author = {H. Hotelling},
  title = {Relations between two sets of variables},
  journal = {Biometrika},
  year = {1936},
  volume = {28},
  pages = {321 - 377}
}

@ARTICLE{HotellingPCA33,
  author = {H. Hotelling},
  title = {Analysis of a complex of statistical variables into principal components},
  journal = {J. Educat. Psychol.},
  year = {1933},
  volume = {24},
  pages = {417 - 441 and 498 - 520}
}

@ARTICLE{HouBBB03,
  author = {T. Hou and X. Xu},
  title = {ADME Evaluation in Drug Discovery. 3. Modelling Blood-Brain Barrier
	Partitioning Using Simple Molecular Descriptors},
  journal = {J. Chem. Inf. Comput. Sci.},
  year = {2003},
  volume = {43},
  pages = {2137 - 2152},
  number = {6}
}

@ARTICLE{Huber2002VSN,
  author = {W. Huber and A. Heydebreck and H. S\"ultmann and A. Poustka and M.
	Vingron},
  title = {Variance Stabilization Applied to Microarray Data Calibration and
	to the Quantification of Differential Expression},
  journal = {Bioinformatics},
  year = {2002},
  volume = {18},
  pages = {S96 - S104},
  owner = {froehlih},
  timestamp = {2006.11.30}
}

@MISC{fibroblastData,
  author = {{Human Fibroblast Serum Response Dataset}},
  howpublished = {\\http://genome-www.stanford.edu/serum/data.html},
  year = {1999},
  groupsearch = {0},
  keywords = {biological datasets}
}

@MISC{Human1999,
  author = {{Human Fibroblast Serum Response Dataset}},
  howpublished = {\\http://genome-www.stanford.edu/serum/data.html},
  year = {1999},
  groupsearch = {0},
  keywords = {biological datasets},
  owner = {froehlih},
  timestamp = {2008.10.02}
}

@ARTICLE{Huuskonen00,
  author = {J. Huuskonen},
  title = {{Estimation of Aqueous Solubility for Diverse Set of Organic Compounds
	Based on Molecular Topology}},
  journal = {J. Chem. Inf. Comput. Sci.},
  year = {2000},
  volume = {40},
  pages = {773-777}
}

@ARTICLE{hvidsten03,
  author = {T. Hvidsten and A. Laegreid and J. Komorowski},
  title = {Learning rule-based models of biological process from gene expression
	time profiles using {G}ene {O}ntology},
  journal = {Bioinformatics},
  year = {2003},
  volume = {19},
  pages = {1116-1123},
  number = {9},
  keywords = {gene ontology supervised methods}
}

@ARTICLE{Ideker2001,
  author = {T. Ideker and V. Thorsson and J. A. Ranish and R. Christmas and J.
	Buhler and J. K. Eng and R. Bumgarner and D. R. Goodlett and R. Aebersold
	and L. Hood},
  title = {Integrated genomic and proteomic analyses of a systematically perturbed
	metabolic network.},
  journal = {Science},
  year = {2001},
  volume = {292},
  pages = {929--934},
  number = {5518},
  month = {May},
  abstract = {We demonstrate an integrated approach to build, test, and refine a
	model of a cellular pathway, in which perturbations to critical pathway
	components are analyzed using DNA microarrays, quantitative proteomics,
	and databases of known physical interactions. Using this approach,
	we identify 997 messenger RNAs responding to 20 systematic perturbations
	of the yeast galactose-utilization pathway, provide evidence that
	approximately 15 of 289 detected proteins are regulated posttranscriptionally,
	and identify explicit physical interactions governing the cellular
	response to each perturbation. We refine the model through further
	iterations of perturbation and global measurements, suggesting hypotheses
	about the regulation of galactose utilization and physical interactions
	between this and a variety of other metabolic pathways.},
  doi = {10.1126/science.292.5518.929},
  institution = {The Institute for Systems Biology, 4225 Roosevelt Way NE, Suite 200,
	Seattle, WA 98105, USA. tideker@systemsbiology.org},
  keywords = {Computational Biology; Culture Media; Databases, Factual; Fungal Proteins,
	metabolism; Galactose, metabolism; Galactosephosphates, metabolism;
	Gene Expression Profiling; Gene Expression Regulation, Fungal; Genome,
	Fungal; Models, Biological; Models, Genetic; Monosaccharide Transport
	Proteins, metabolism; Mutation; Oligonucleotide Array Sequence Analysis;
	Proteome; RNA, Fungal, genetics/metabolism; RNA, Messenger, genetics/metabolism;
	Saccharomyces cerevisiae Proteins; Saccharomyces cerevisiae, genetics/metabolism},
  owner = {holfro},
  pii = {292/5518/929},
  pmid = {11340206},
  timestamp = {2009.04.24},
  url = {http://dx.doi.org/10.1126/science.292.5518.929}
}

@CONFERENCE{Imoto2003NetworkPrior,
  author = {Imoto, S., Higuchi and T., Goto, T. and Tashiro, K. and Kuhara, S.
	and Miyano, S.},
  title = {Combining Microarrays and Biological Knowledge for Estimating Gene
	Networks via Bayesian Networks},
  booktitle = {Proc. 2nd Computational Systems Bioinformatics},
  year = {2003},
  pages = {104 - 113},
  journal = {Proc. 2nd Computational Systems Bioinformatics},
  owner = {froehlih},
  timestamp = {2007.12.14}
}

@CONFERENCE{Inokuchi00,
  author = {A. Inokuchi and T. Washio and H. Motoda},
  title = {An Apriori-based algorithm for mining frequent substructures from
	graph data},
  booktitle = {Proc. 4th PKDD},
  year = {2000},
  pages = {13 - 23}
}

@ARTICLE{Ivanova2006,
  author = {Natalia Ivanova and Radu Dobrin and Rong Lu and Iulia Kotenko and
	John Levorse and Christina DeCoste and Xenia Schafer and Yi Lun and
	Ihor R Lemischka},
  title = {Dissecting self-renewal in stem cells with RNA interference.},
  journal = {Nature},
  year = {2006},
  volume = {442},
  pages = {533--538},
  number = {7102},
  month = {Aug},
  abstract = {We present an integrated approach to identify genetic mechanisms that
	control self-renewal in mouse embryonic stem cells. We use short
	hairpin RNA (shRNA) loss-of-function techniques to downregulate a
	set of gene products whose expression patterns suggest self-renewal
	regulatory functions. We focus on transcriptional regulators and
	identify seven genes for which shRNA-mediated depletion negatively
	affects self-renewal, including four genes with previously unrecognized
	roles in self-renewal. Perturbations of these gene products are combined
	with dynamic, global analyses of gene expression. Our studies suggest
	specific biological roles for these molecules and reveal the complexity
	of cell fate regulation in embryonic stem cells.},
  doi = {10.1038/nature04915},
  institution = {Department of Molecular Biology, Princeton University, Princeton,
	New Jersey 08544, USA. nivanova@molbio.princeton.edu},
  keywords = {Animals; Cell Differentiation; Cell Line; Cell Proliferation; DNA-Binding
	Proteins; Embryo, Mammalian; Gene Expression; Genetic Complementation
	Test; Homeodomain Proteins; Mice; RNA Interference; Regeneration;
	Stem Cells},
  owner = {frohlich},
  pii = {nature04915},
  pmid = {16767105},
  timestamp = {2012.01.30},
  url = {http://dx.doi.org/10.1038/nature04915}
}

@ARTICLE{iyer99,
  author = {V. Iyer and M. Eisen and D. Ross and G. Schuler and T. Moore and
	J. Lee and J. Trent and L. Staudt and J. Hudson Jr, and M. Boguski
	and D. Lashkari and D. Shalon and D. Botstein and P. Brown},
  title = {The Transcriptional Program in Response of Human Fibroblasts to Serum},
  journal = {Science},
  year = {1999},
  volume = {283},
  pages = {83-87},
  groupsearch = {0},
  keywords = {original papers on biological datasets}
}

@CONFERENCE{JakHau99,
  author = {T. S. Jaakkola and D. Haussler},
  title = {Probalistic kernel regression models},
  booktitle = {Proc. 1999 Conf. AI and Statistics},
  year = {1999}
}

@BOOK{jain88,
  title = {Algorithms for Clustering Data},
  publisher = {Prentice Hall},
  year = {1988},
  author = {Jain, A.K. and Dubes, R.C.},
  address = {Englewood Cliffs, New Jersey 07632},
  groupsearch = {0},
  keywords = {algorithmic books}
}

@BOOK{jainBookClustering88,
  title = {Algorithms for Clustering Data},
  publisher = {Prentice-Hall},
  year = {1988},
  author = {A. Jain and R. Dubes},
  address = {Englewood Cliffs, NJ}
}

@ARTICLE{jain99data,
  author = {A. K. Jain and M. N. Murty and P. J. Flynn},
  title = {Data clustering: a review},
  journal = {ACM Computing Surveys},
  year = {1999},
  volume = {31},
  pages = {264--323},
  number = {3},
  url = {citeseer.ist.psu.edu/jain99data.html}
}

@INPROCEEDINGS{Jiang98,
  author = {J. Jiang and D. Conrath},
  title = {Semantic similarity based on corpus statistics and lexical taxonomy},
  booktitle = {Proceedings of the International Conference on Research in Computational
	Linguistics},
  year = {1998},
  address = {Taiwan},
  groupsearch = {0},
  keywords = {semantic distances}
}

@CONFERENCE{JoachimsTransSVM03,
  author = {T. Joachims},
  title = {Transductive Learning via Spectral Graph Partitioning},
  booktitle = {Int. Conf. Machine Learning},
  year = {2003}
}

@CONFERENCE{JoachimsXiAlpha00,
  author = {T. Joachims},
  title = {Estimating the Generalization Performance of a SVM Efficiently},
  booktitle = {Proc. Int. Conf. Machine Learning},
  year = {2000},
  publisher = {Morgan Kaufmann}
}

@CONFERENCE{JohKohPfl94,
  author = {G. John and R. Kohavi and K. Pfleger},
  title = {Irrelevant features and the subset selection problem},
  booktitle = {Machine Learning: Proc. 11th Int. Conf.},
  year = {1994},
  pages = {121 - 129},
  publisher = {Morgan Kaufmann}
}

@ARTICLE{PerttunenDIRECT93,
  author = {D. Jones and C. Perttunnen and B. Stuckman},
  title = {Lipschitzian Optimization without the Lipschitz Constant},
  journal = {J. Optimization Theory and Applications},
  year = {1993},
  volume = {79},
  pages = {157 - 181},
  number = {1}
}

@ARTICLE{JonesEGO98,
  author = {D. Jones and M. Schonlau and W. Welch},
  title = {Efficient Global Optimization of Expensive Black-Box Functions},
  journal = {J. Global Optimization},
  year = {1998},
  volume = {13},
  pages = {455 - 492}
}

@ARTICLE{joslyn04categorizer,
  author = {C.A. Joslyn and S.M. Mniszewski and A. Fulmer and G. Heaton},
  title = {The Gene Ontology Categorizer},
  journal = {Bioinformatics},
  year = {2004},
  volume = {20},
  pages = {i169-i177},
  number = {1},
  doi = {10.1093/bioinformatics/bth921}
}

@ARTICLE{Kanehisa2008KEGG,
  author = {Kanehisa, M. and Araki, M. and Goto, S. and Hattori, M. and Hirakawa,
	M. and Itoh, M. and Katayama, T. and Kawashima, S. and Okuda, S.
	and Tokimatsu, T. and Yamanishi, Y.},
  title = {KEGG for linking genomes to life and the environment},
  journal = {Nucleic Acids Res.},
  year = {2008},
  volume = {36},
  pages = {D480 - D484},
  owner = {froehlih},
  timestamp = {2008.03.17}
}

@CONFERENCE{KannanSpectral00,
  author = {R. Kannan and S. Vempala},
  title = {On clusterings - good, bad and spectral},
  booktitle = {Proc. Symp. Found. Comp. Sci.},
  year = {2000},
  pages = {367 - 377}
}

@ARTICLE{KansyHIA98,
  author = {M. Kansy and F. Senner and K. Gubernator},
  title = {Physicochemical High Throughput Screening: Parallel Artificial Membrane
	Permeation Assay in the Description of Passive Absorption Processes},
  journal = {J. Med. Chem.},
  year = {1998},
  volume = {41},
  pages = {1007 - 1010}
}

@INCOLLECTION{KashimaSchoelkpfbook04,
  author = {H. Kashima and K. Tsuda and A. Inokuchi},
  title = {Kernels for Graphs},
  booktitle = {Kernel Methods in Computational Biology},
  publisher = {MIT Press},
  year = {2004},
  editor = {B. Sch\"olkopf and K. Tsuda and J.-P. Vert},
  pages = {155 - 170},
  address = {Cambridge, MA}
}

@CONFERENCE{Kashima03,
  author = {H. Kashima and K. Tsuda and A. Inokuchi},
  title = {Marginalized Kernels Between Labeled Graphs},
  booktitle = {Proc. 20th Int. Conf. on Machine Learning},
  year = {2003}
}

@BOOK{Kaufman1990ClusteringBook,
  title = {Finding Groups in Data: An Introduction to Cluster Analysis},
  publisher = {Wiley},
  year = {1990},
  author = {L. Kaufman and P. Rousseeuw},
  address = {New York},
  owner = {froehlih},
  timestamp = {2007.02.07}
}

@ARTICLE{Kersey2004IPI,
  author = {D. Kersey and J. Duarte and A. Williams and Y. Karavidopoulou and
	E. Birney and R. Apweiler},
  title = {{The International Protein Index: an integrated database for proteomics
	experiments}},
  journal = {Proteomics},
  year = {2004},
  volume = {4},
  pages = {1985 - 1988},
  number = {7},
  owner = {froehlih},
  timestamp = {2008.03.17}
}

@ARTICLE{KimWahba71,
  author = {G. Kimeldorf and G. Wahba},
  title = {Some results on Tchebycheffian spline functions},
  journal = {J. Math. Anal. and Appl.},
  year = {1971},
  volume = {33},
  pages = {82 - 95}
}

@CONFERENCE{KirRen92,
  author = {K. Kira and L. Rendell},
  title = {{A practical approach to feature selection}},
  booktitle = {Proc. Int. Conf. Machine Learning},
  year = {1992},
  pages = {249 - 256}
}

@ARTICLE{Kirkpatrick1983SimAnn,
  author = {S. Kirkpatrick and C. D. Gelatt and M. P. Vecchi},
  title = {Optimization by Simulated Annealing},
  journal = {Science},
  year = {1983},
  volume = {220},
  pages = {671 - 680},
  number = {4598},
  owner = {froehlih},
  timestamp = {2006.11.28}
}

@ARTICLE{Kohavi97,
  author = {R. Kohavi and G. John},
  title = {{Wrappers for Feature Subset Selection}},
  journal = {Artificial Intelligence},
  year = {1997},
  volume = {97},
  pages = {273 - 324},
  number = {12}
}

@BOOK{KohonenSOMs1995,
  title = {Self Organizing Maps},
  publisher = {Springer},
  year = {1995},
  author = {T. Kohonen},
  address = {Berlin},
  owner = {froehlih},
  timestamp = {2006.10.26}
}

@CONFERENCE{Kononenko94,
  author = {I. Kononenko},
  title = {{Estimating attributes: Analysis and extensions of RELIEF}},
  booktitle = {Proc. Europ. Conf. Machine Learning},
  year = {1994},
  pages = {171 - 182}
}

@ARTICLE{KonHon97,
  author = {I. Kononenko and S. J. Hong},
  title = {{Attribute Selection for Modeling}},
  journal = {Future Generation Computer Systems},
  year = {1997},
  volume = {13},
  pages = {181 - 195},
  number = {2 - 3}
}

@CONFERENCE{KrishnaJCFO003,
  author = {B. Krishnapuram and L. Carin and A. Hartemink},
  title = {Joint classifier and feature optimization for cancer diagnosis using
	gene expression data},
  booktitle = {Proc. An. Int. Conf. Research in computational molecular biology},
  year = {2003},
  pages = {167 - 175},
  publisher = {ACM Press, NY}
}

@INPROCEEDINGS{kruskal56,
  author = {Kruskal, J.B},
  title = {On the shortest spanning subtree of a graph and the travelling salesman
	problem},
  booktitle = {Proc. Amer. Math. Soc.},
  year = {1956},
  volume = {7},
  pages = {48-50},
  groupsearch = {0},
  keywords = {minimum spanning trees}
}

@CONFERENCE{KubinyiReview04,
  author = {H. Kubinyi},
  title = {Changing Paradigms in Drug Discovery},
  booktitle = {Proc. Int. Beilstein Workshop},
  year = {2004},
  editor = {M. Hicks et al.},
  pages = {51 - 72},
  address = {Berlin},
  publisher = {Logos-Verlag}
}

@ARTICLE{KubinyiDrugResearch03,
  author = {H. Kubinyi},
  title = {{Drug research: myths, hype and reality}},
  journal = {Nature Reviews: Drug Discovery},
  year = {2003},
  volume = {2},
  pages = {665-668},
  file = {kub03.pdf:kub03.pdf:PDF},
  groupsearch = {0},
  url = {http://home.t-online.de/home/kubinyi/nrdd-pub-08-03.pdf}
}

@ARTICLE{KubinyiQSARHistory02,
  author = {H. Kubinyi},
  title = {{F}rom {N}arcosis to {H}yperspace: {T}he {H}istory of {QSAR}},
  journal = {Quant. Struct. Act. Relat.},
  year = {2002},
  volume = {21},
  pages = {348-356},
  file = {kub02.pdf:kub02.pdf:PDF},
  groupsearch = {0}
}

@ARTICLE{KuhnMunkres55,
  author = {H. Kuhn},
  title = {The Hungarian Method for the Assignment Problem},
  journal = {Naval Res. Logist. Quart.},
  year = {1955},
  volume = {2},
  pages = {83 - 97}
}

@CONFERENCE{KKTTheorem51,
  author = {H. Kuhn and A. Tucker},
  title = {Nonlinear programming},
  booktitle = {Proc. 2nd Berkely Symposium on Mathematical Statistics and Probabilistics},
  year = {1951},
  pages = {481 - 492},
  address = {Berkley},
  publisher = {University of California Press}
}

@INPROCEEDINGS{kurhekar02,
  author = {Kurhekar, M.P. and Adak, S. and Jhunjhunwala, S. and Raghupathy,
	K.},
  title = {Genome-wide pathway analysis and visulization using gene expression
	data},
  booktitle = {Proceedings of the Pacific Symposium on Biocomputing},
  year = {2002},
  pages = {462-473},
  groupsearch = {0},
  keywords = {pathway scoring methods}
}

@ARTICLE{KwokEvidenceFrameWorkSVM00,
  author = {J. Kwok},
  title = {The Evidence Framework Applied to Support Vector Machines},
  journal = {IEEE Transactions on Neural Networks},
  year = {2000},
  volume = {11},
  pages = {1162 - 1173},
  number = {5}
}

@ARTICLE{laiKCCA00,
  author = {P. Lai and C. Fyfe},
  title = {Kernel and nonlinear canonical correlation analysis},
  journal = {Int. Journal of Neural Systems},
  year = {2000},
  volume = {10},
  pages = {365 - 377},
  number = {5}
}

@ARTICLE{Cris04,
  author = {G. Lanckriet and N. Cristianini and P. Bartlett and L. El Ghaoui
	and M. Jordan},
  title = {Learning the Kernel Matrix with Semidefinite Programming},
  journal = {J. Machine Learning Research},
  year = {2004},
  volume = {5},
  pages = {27 - 72}
}

@ARTICLE{LangeStability,
  author = {Tilman Lange and Volker Roth and Mikio L. Braun and Joachim M. Buhmann},
  title = {Stability-based validation of clustering solutions},
  journal = {Neural Computation},
  year = {2004},
  volume = {16},
  pages = {1299--1323},
  number = {6},
  doi = {http://dx.doi.org/10.1162/089976604773717621},
  issn = {0899-7667},
  publisher = {MIT Press}
}

@CONFERENCE{KwokBayesianSVR01,
  author = {M. Law and J. Kwok},
  title = {Bayesian Support Vector Regression},
  booktitle = {Proc. 11th Int. Workshop on AI and Statistics (AISTATS 2001)},
  year = {2001},
  pages = {239 - 244}
}

@ARTICLE{Le2005,
  author = {Xiao-Feng Le and Franz Pruefer and Robert C Bast},
  title = {HER2-targeting antibodies modulate the cyclin-dependent kinase inhibitor
	p27Kip1 via multiple signaling pathways.},
  journal = {Cell Cycle},
  year = {2005},
  volume = {4},
  pages = {87--95},
  number = {1},
  month = {Jan},
  abstract = {Anti-HER2 antibody trastuzumab is emerging as a frontline therapy
	for patients with metastatic breast cancers that overexpress HER2.
	Understanding the molecular mechanisms by which the antibody inhibits
	tumor growth should permit the design of even more effective trastuzumab-based
	protocols. Several groups including our own have demonstrated that
	induction of cyclin-dependent kinase (CDK) inhibitor p27Kip1 protein
	is one of the key mechanisms of action of HER2-targeting antibodies.
	In this review, we discuss currently available data regarding the
	multiple signaling targets and pathways by which HER2-targeting antibodies
	upregulate p27Kip1 protein in breast cancer cells that overexpress
	HER2. Anti-HER2 antibodies inhibit HER2-mediated signaling in cancer
	cells, ultimately upregulating the levels and activity of p27Kip1
	protein. At least six signaling targets and pathways are modulated
	by trastuzumab. By inhibiting CDK2 and decreasing Thr187 phosphorylation
	of p27Kip1, trastuzumab abrogates targeting of SCF-ubiquitin E3 ligase
	and minimizes proteasome degradation of p27Kip1. By inhibiting AKT
	and human kinase interacting stathmin (hKIS), trastuzumab blocks
	Thr157-, Thr198- and Ser10-induced p27Kip1 translocation from the
	nucleus to the cytosol, which increases the inhibitory effect of
	p27Kip1. By inhibiting Jun activation domain-binding protein 1 (Jab1)
	trastuzumab increases nuclear retention of p27Kip1. By inhibiting
	cyclin D and c-Myc, trastuzumab releases the sequestrated p27bKip1
	protein from cyclin D-CDK4/6 complexes and increase the effect of
	p27Kip1 on CDK2-cyclin E complexes. By stimulating minibrain related
	kinase (MIRK), trastuzumab stabilizes p27Kip1 in the nucleus, which
	increases inhibitory action of p27Kip1 on CDK2. The targets and pathways
	affected by trastuzumab work in concert to maximize the expression
	and inhibitory effect of p27Kip1, which leads to cell cycle G1 arrest
	and growth inhibition.},
  institution = {Department of Experimental Therapeutics, Division of Cancer Medicine,
	University of Texas M.D. Anderson Cancer Center, Houston, Texas,
	USA.},
  keywords = {Antibodies, Monoclonal; Breast Neoplasms; Cell Cycle; Cyclin-Dependent
	Kinase Inhibitor p27; Cyclin-Dependent Kinases; Cyclins; Gene Expression
	Regulation, Neoplastic; Humans; Mitogen-Activated Protein Kinases;
	Phosphorylation; Protein-Serine-Threonine Kinases; Protein-Tyrosine
	Kinases; Proto-Oncogene Proteins c-myc; Receptor, erbB-2; Signal
	Transduction; Transcription Factors; Up-Regulation},
  owner = {holger},
  pii = {1360},
  pmid = {15611642},
  timestamp = {2008.11.01}
}

@ARTICLE{Lee1999NMF,
  author = {Daniel D. Lee and H. Sebastian Seung},
  title = {Learning the parts of objects by non-negative matrix factorization},
  journal = {Nature},
  year = {1999},
  volume = {401},
  pages = {788 - 791},
  number = {6755},
  owner = {froehlih},
  timestamp = {2008.01.10}
}

@ARTICLE{lee04,
  author = {S. Lee and J. Hur and Y. Kim},
  title = {A graph-theoretic modeling on GO space for biological interpretation
	on gene clusters},
  journal = {Bioinformatics},
  year = {2004},
  volume = {20},
  pages = {381-388},
  number = {3},
  doi = {10.1093/bioinformatics/btg420}
}

@ARTICLE{lenhard01genelynx,
  author = {B. Lenhard and W.S. Hayes and W.W. Wassermann},
  title = {{G}ene{L}ynx: A Gene-Centric Portal to the Human Genome},
  journal = {Genome Research},
  year = {2001},
  volume = {11},
  pages = {2151-2157},
  number = {12},
  month = {December}
}

@ARTICLE{Lerman2007ManifoldEmbedding,
  author = {Gilad Lerman and Boris E Shakhnovich},
  title = {Defining functional distance using manifold embeddings of gene ontology
	annotations.},
  journal = {Proc Natl Acad Sci U S A},
  year = {2007},
  volume = {104},
  pages = {11334--11339},
  number = {27},
  month = {Jul},
  abstract = {Although rigorous measures of similarity for sequence and structure
	are now well established, the problem of defining functional relationships
	has been particularly daunting. Here, we present several manifold
	embedding techniques to compute distances between Gene Ontology (GO)
	functional annotations and consequently estimate functional distances
	between protein domains. To evaluate accuracy, we correlate the functional
	distance to the well established measures of sequence, structural,
	and phylogenetic similarities. Finally, we show that manual classification
	of structures into folds and superfamilies is mirrored by proximity
	in the newly defined function space. We show how functional distances
	place structure-function relationships in biological context resulting
	in insight into divergent and convergent evolution. The methods and
	results in this paper can be readily generalized and applied to a
	wide array of biologically relevant investigations, such as accuracy
	of annotation transference, the relationship between sequence, structure,
	and function, or coherence of expression modules.},
  doi = {10.1073/pnas.0702965104},
  keywords = {Evolution, Molecular; Models, Genetic; Protein Structure, Tertiary;
	Proteins; Sequence Homology, Amino Acid; Sequence Homology, Nucleic
	Acid; Structure-Activity Relationship},
  owner = {froehlih},
  pii = {0702965104},
  pmid = {17595300},
  timestamp = {2008.08.21},
  url = {http://dx.doi.org/10.1073/pnas.0702965104}
}

@INCOLLECTION{LeslieStringKernels04,
  author = {C. Leslie and R. Kuang and E. Eskin},
  title = {Inexact Matching String Kernels for Protein Classification},
  booktitle = {Kernel Methods in Computational Biology},
  publisher = {MIT Press},
  year = {2004},
  editor = {B. Sch\"olkopf and K. Tsuda and J.-P. Vert},
  pages = {95 - 112},
  address = {Cambridge, MA}
}

@ARTICLE{Liao2004localFDR,
  author = {J. Liao and Y. Lin and Z. Selvanayagam and W. Shih},
  title = {A mixture model for estimating the local false discovery rate in
	DNA microarray analysis},
  journal = {Bioinformatics},
  year = {2004},
  volume = {20},
  pages = {2694 - 2701},
  number = {16},
  owner = {froehlih},
  timestamp = {2007.02.02}
}

@INPROCEEDINGS{Lin98,
  author = {D. Lin},
  title = {An information-theoretic definition of similarity},
  booktitle = {Proceedings of the 15th International Conference on Machine Learning},
  year = {1998},
  editor = {Morgan Kaufmann},
  volume = {1},
  pages = {296-304},
  address = {San Francisco, CA},
  groupsearch = {0},
  keywords = {semantic distances}
}

@ARTICLE{LipRule97,
  author = {C. Lipinski and F. Lombardo and B. Dominy and P. Feeney},
  title = {Experimental and computational approaches to estimate solubility
	and permeability in drug discovery and development settings},
  journal = {Adv. Drug Deliv. Rev.},
  year = {1997},
  volume = {23},
  pages = {3 - 26}
}

@BOOK{Ljung1999SystemIdentification,
  title = {System Identification - Theory for the User},
  publisher = {Prentice Hall},
  year = {1999},
  author = {L. Ljung},
  owner = {froehlih},
  timestamp = {2008.02.19}
}

@INPROCEEDINGS{Lord03,
  author = {Lord, P.W. and Stevens, R.D. and Brass, A. and Goble, C.A.},
  title = {Semantic Similarity Measures as Tools for Exploring the Gene Ontology},
  booktitle = {Proceedings of the Pacific Symposium on Biocomputing},
  year = {2003},
  pages = {601-612},
  groupsearch = {0},
  keywords = {semantic distances}
}

@ARTICLE{Lord02,
  author = {Lord, P.W. and Stevens, R.D. and Brass, A. and Goble, C.A.},
  title = {Semantic Similarity Measures across the Gene Ontology: the relationship
	between sequence and annotation},
  journal = {Bioinformatics},
  year = {2002},
  volume = {19},
  pages = {1275-1283},
  groupsearch = {0},
  keywords = {semantic distances}
}

@ARTICLE{AlexanderVLukashin05012001SimAnn,
  author = {Lukashin, Alexander V. and Fuchs, Rainer},
  title = {{Analysis of temporal gene expression profiles: clustering by simulated
	annealing and determining the optimal number of clusters}},
  journal = {Bioinformatics},
  year = {2001},
  volume = {17},
  pages = {405-414},
  number = {5},
  abstract = {Motivation: Cluster analysis of genome-wide expression data from DNA
	microarray hybridization studies has proved to be a useful tool for
	identifying biologically relevant groupings of genes and samples.
	In the present paper, we focus on several important issues related
	to clustering algorithms that have not yet been fully studied. Results:
	We describe a simple and robust algorithm for the clustering of temporal
	gene expression profiles that is based on the simulated annealing
	procedure. In general, this algorithm guarantees to eventually find
	the globally optimal distribution of genes over clusters. We introduce
	an iterative scheme that serves to evaluate quantitatively the optimal
	number of clusters for each specific data set. The scheme is based
	on standard approaches used in regular statistical tests. The basic
	idea is to organize the search of the optimal number of clusters
	simultaneously with the optimization of the distribution of genes
	over clusters. The efficiency of the proposed algorithm has been
	evaluated by means of a reverse engineering experiment, that is,
	a situation in which the correct distribution of genes over clusters
	is known a priori. The employment of this statistically rigorous
	test has shown that our algorithm places greater than 90% genes into
	correct clusters. Finally, the algorithm has been tested on real
	gene expression data (expression changes during yeast cell cycle)
	for which the fundamental patterns of gene expression and the assignment
	of genes to clusters are well understood from numerous previous studies.
	Availability: The source code of the program implementing the algorithm
	is available upon request from the authors. Contact: alex_lukashin@biogen.com},
  doi = {10.1093/bioinformatics/17.5.405},
  eprint = {http://bioinformatics.oxfordjournals.org/cgi/reprint/17/5/405.pdf},
  url = {http://bioinformatics.oxfordjournals.org/cgi/content/abstract/17/5/405}
}

@ARTICLE{LuxCompression04,
  author = {U. Luxburg and O. Bousquet and B. Sch\"olkopf},
  title = {A Compression Approach to Support Vector Model Selection},
  journal = {J. Machine Learning Research},
  year = {2004},
  volume = {5},
  pages = {293 - 323}
}

@ARTICLE{Lahdesmaki2003LearningBooleanNetworks,
  author = {H. L{\"a}hdesm{\"a}ki and I. Shmulevich and O. Yli-Harja},
  title = {{On Learning Gene Regulatory Networks Under the Boolean Network Model}},
  journal = {Machine Learning},
  year = {2003},
  volume = {52},
  pages = {147 - 167},
  file = {:http\://shmulevich.systemsbiology.net/downloads/BNLearnML.pdf:PDF},
  owner = {holger},
  timestamp = {2009.01.18}
}

@INPROCEEDINGS{yalmip,
  author = {J. L{\"o}fberg},
  title = {{YALMIP} : A Toolbox for Modeling and Optimization in {MATLAB}},
  booktitle = {Proceedings of the {CACSD} Conference},
  year = {2004},
  address = {Taipei, Taiwan},
  note = {Available from http://control.ee.ethz.ch/$\sim$joloef/yalmip.php}
}

@ARTICLE{Loebke2008,
  author = {Christian L�bke and Mark Laible and Claudia Rappl and Markus Ruschhaupt
	and Ozg�r Sahin and Dorit Arlt and Stefan Wiemann and Annemarie
	Poustka and Holger S�ltmann and Ulrike Korf},
  title = {Contact spotting of protein microarrays coupled with spike-in of
	normalizer protein permits time-resolved analysis of ERBB receptor
	signaling.},
  journal = {Proteomics},
  year = {2008},
  volume = {8},
  pages = {1586--1594},
  number = {8},
  month = {Apr},
  abstract = {Protein microarrays allow highly accurate comparison and quantification
	of numerous biological samples in parallel while requiring only little
	material. This qualifies protein arrays for systems biology and clinical
	research where only limited sample material is available, but a precise
	readout is required. With the introduction of signal normalization
	steps to monitor the drop size of manually contact-spotted RP protein
	arrays, the usefulness of normalizer proteins to ensure a high-throughput
	but inexpensive protein analysis was demonstrated. This approach
	was applied for the analysis of signaling through ERBB receptor activated
	kinases in the breast cancer cell line MCF-7. Activation of ERK1/2
	and AKT by ERBB1 (EGFR), ERRB2 (HER2/neu), and ERBB3-4 was monitored
	in a time-resolved manner. Analysis of pathway activation by stimulation
	with epidermal growth factor and heregulin, or inhibition by blocking
	with gefitinib or herceptin allowed a characterization of the distinct
	signaling properties of the different ERBB receptor subtypes.},
  doi = {10.1002/pmic.200700733},
  keywords = {Antibodies, Monoclonal; Breast Neoplasms; Epidermal Growth Factor;
	Glutathione Transferase; Humans; Mitogen-Activated Protein Kinase
	1; Mitogen-Activated Protein Kinase 3; Protein Array Analysis; Proto-Oncogene
	Proteins c-akt; Quinazolines; Receptor, Epidermal Growth Factor;
	Receptor, erbB-2; Recombinant Fusion Proteins; Reference Standards;
	Signal Transduction; Tumor Cells, Cultured},
  owner = {froehlih},
  pmid = {18351692},
  timestamp = {2008.10.23},
  url = {http://dx.doi.org/10.1002/pmic.200700733}
}

@CONFERENCE{MacKayGP97,
  author = {D. MacKay},
  title = {{Gaussian Processes - A Replacement for Supervised Neural Networks?}},
  booktitle = {Proc. Neural Inf. Proc. Syst.},
  year = {1997},
  note = {Lecture note}
}

@ARTICLE{MacKayEvidence92,
  author = {D. MacKay},
  title = {{The Evidence Framework Applied to Classification Networks}},
  journal = {Neural Computation},
  year = {1992},
  volume = {4},
  pages = {720 - 736},
  number = {5}
}

@INCOLLECTION{MaggioraChemSim04,
  author = {G. Maggiora and V. Shanmugasundaram},
  title = {Molecular Similarity Measures},
  booktitle = {Chemoinformatics},
  publisher = {Humana Press},
  year = {2004},
  editor = {J. Bajorath},
  pages = {1 - 50}
}

@ARTICLE{Maglott2007Entrez,
  author = {D. Maglott and J. Ostell and K. Pruitt and T. Tatusova},
  title = {{Entrez: Gene-Centered Informaiton at NCBI}},
  journal = {Nucleic Acids Res.},
  year = {2007},
  volume = {35},
  pages = {D26 - D31},
  owner = {froehlih},
  timestamp = {2008.03.17}
}

@INCOLLECTION{Waterbeemd03BookBioavail,
  author = {A. Mandagere and B. Jones},
  title = {Prediction of Bioavailability},
  booktitle = {Drug Bioavailability},
  publisher = {Wiley-VCH},
  year = {2003},
  editor = {H. van de Waterbeemd and H. Lennern\"as and P. Artursson},
  pages = {444 - 460},
  address = {Weinheim}
}

@PHDTHESIS{Markowetz2006Thesis,
  author = {F. Markowetz},
  title = {Probabilistic Models for Gene Silencing Data},
  school = {Free University Berlin},
  year = {2006},
  owner = {froehlih},
  timestamp = {2007.09.24}
}

@ARTICLE{Markowetz2005Inference,
  author = {F. Markowetz and J. Bloch and R. Spang},
  title = {Non-transcriptional pathway features reconstructed from secondary
	effects of RNA interference},
  journal = {Bioinformatics},
  year = {2005},
  volume = {21},
  pages = {4026 - 4032},
  number = {21},
  owner = {froehlih},
  timestamp = {2006.11.28}
}

@ARTICLE{Markowetz2007,
  author = {F. Markowetz and D. Kostka and O. Troyanskaya and R. Spang},
  title = {Nested Effects Models for High-dimensional Phenotyping Screens},
  journal = {Bioinformatics},
  year = {2007},
  volume = {23},
  pages = {i305 - i312},
  file = {MarkowetzISMB07.pdf:/home/froehlih/Papers/Artikel/MarkowetzISMB07.pdf:PDF},
  owner = {froehlih},
  timestamp = {2006.11.28}
}

@INCOLLECTION{Markowetz2003BayesNet,
  author = {F. Markowetz and R. Spang},
  title = {{Evaluating the Effect of Perturbations in Reconstructing Network
	Topologies}},
  booktitle = {Proc. 3rd Int. Workshop on Distr. Stat. Comp. (DSC 2003)},
  year = {2003},
  __markedentry = {[froehlih]},
  owner = {froehlih},
  timestamp = {2008.02.18}
}

@INCOLLECTION{martinPharamcophore98,
  author = {Y. Martin},
  title = {{P}harmacophore mapping},
  booktitle = {Designing Bioactive Molecules},
  publisher = {Oxford University Press},
  year = {1998},
  editor = {Y. Martin and P. Willett},
  pages = {121--148}
}

@ARTICLE{MartinSim02,
  author = {Y. Martin and J. Kofron and L. Traphagen},
  title = {Do Structurally Similar Molecules Have Similar Biological Activity?},
  journal = {J. Med. Chem.},
  year = {2002},
  volume = {45},
  pages = {4350 - 4358}
}

@BOOK{LEDABook99,
  title = {{The LEDA Platform of Combinatorial and Geometric Computing}},
  publisher = {Cambridge University Press},
  year = {1999},
  author = {K. Mehlhorn and S. N\"aher}
}

@CONFERENCE{meila00learning,
  author = {M. Meila and J. Shi},
  title = {Learning segmentation by random walks},
  booktitle = {Adv. Neural Inf. Proc. Syst. 13},
  year = {2001},
  pages = {873-879}
}

@ARTICLE{meila01random,
  author = {M. Meila and J. Shi},
  title = {A random walks view of spectral segmentation},
  journal = {AI and Statistics (AISTATS)},
  year = {2001}
}

@ARTICLE{Melvin2007HierClassif,
  author = {I. Melvin and E. Ie and J. Weston and W. Noble and C. Leslie},
  title = {{Multi-class Protein Classification Using Adaptive Codes}},
  journal = {J. Machine Learning Research},
  year = {2007},
  volume = {8},
  pages = {1557 - 1581},
  owner = {froehlih},
  timestamp = {2008.03.31}
}

@ARTICLE{Mercer09,
  author = {J. Mercer},
  title = {Functions of positive and negative type and their connection with
	the theory of integral equations},
  journal = {Philosophical Transactions of the Royal Society},
  year = {1909},
  volume = {A 209},
  pages = {415 - 446}
}

@BOOK{michalewicz96,
  title = {Genetic Algorithms + Data Structures = Evolution Programs},
  publisher = {Springer, Berlin},
  year = {1996},
  author = {Michalewicz, Z.},
  groupsearch = {0},
  keywords = {algorithmic books evolutionary algorithms}
}

@ARTICLE{Mistry2008GOFuncSim,
  author = {Meeta Mistry and Paul Pavlidis},
  title = {Gene Ontology term overlap as a measure of gene functional similarity.},
  journal = {BMC Bioinformatics},
  year = {2008},
  volume = {9},
  pages = {327},
  abstract = {BACKGROUND: The availability of various high-throughput experimental
	and computational methods allows biologists to rapidly infer functional
	relationships between genes. It is often necessary to evaluate these
	predictions computationally, a task that requires a reference database
	for functional relatedness. One such reference is the Gene Ontology
	(GO). A number of groups have suggested that the semantic similarity
	of the GO annotations of genes can serve as a proxy for functional
	relatedness. Here we evaluate a simple measure of semantic similarity,
	term overlap (TO). RESULTS: We computed the TO for randomly selected
	gene pairs from the mouse genome. For comparison, we implemented
	six previously reported semantic similarity measures that share the
	feature of using computation of probabilities of terms to infer information
	content, in addition to three vector based approaches and a normalized
	version of the TO measure. We find that the overlap measure is highly
	correlated with the others but differs in detail. TO is at least
	as good a predictor of sequence similarity as the other measures.
	We further show that term overlap may avoid some problems that affect
	the probability-based measures. Term overlap is also much faster
	to compute than the information content-based measures. CONCLUSION:
	Our experiments suggest that term overlap can serve as a simple and
	fast alternative to other approaches which use explicit information
	content estimation or require complex pre-calculations, while also
	avoiding problems that some other measures may encounter.},
  doi = {10.1186/1471-2105-9-327},
  owner = {froehlih},
  pii = {1471-2105-9-327},
  pmid = {18680592},
  timestamp = {2008.08.22},
  url = {http://dx.doi.org/10.1186/1471-2105-9-327}
}

@ARTICLE{Mitra04ActiveLearningSVM,
  author = {P. Mitra and C. Murphy and S. Pal},
  title = {A probabilistic active support vector learning algorithm},
  journal = {Pattern Analysis and Machine Intelligence, IEEE Transactions on},
  year = {2004},
  volume = {26},
  pages = {413 - 418},
  number = {3}
}

@CONFERENCE{BennetPatternSearch02,
  author = {M. Momma and K. Bennett},
  title = {A Pattern Search Method for Model Selection of Support Vector Regression},
  booktitle = {SIAM Conf. on Data Mining},
  year = {2002}
}

@ARTICLE{Moser1999FST,
  author = {Moser, P.C. and Sanger, D.J.},
  title = {{5-HT1A receptor antagonists neither potentiate nor inhibit the effects
	of fluoxetine and befloxatone in forced swim test in rats}},
  journal = {Eur. J. Pharmacol.},
  year = {1999},
  volume = {372},
  pages = {127-134},
  owner = {froehlih},
  timestamp = {2006.07.13}
}

@ARTICLE{Mukherjee2009,
  author = {Sach Mukherjee and Steven Pelech and Richard M Neve and Wen-Lin Kuo
	and Safiyyah Ziyad and Paul T Spellman and Joe W Gray and Terence
	P Speed},
  title = {Sparse combinatorial inference with an application in cancer biology.},
  journal = {Bioinformatics},
  year = {2009},
  volume = {25},
  pages = {265--271},
  number = {2},
  month = {Jan},
  abstract = {MOTIVATION: Combinatorial effects, in which several variables jointly
	influence an output or response, play an important role in biological
	systems. In many settings, Boolean functions provide a natural way
	to describe such influences. However, biochemical data using which
	we may wish to characterize such influences are usually subject to
	much variability. Furthermore, in high-throughput biological settings
	Boolean relationships of interest are very often sparse, in the sense
	of being embedded in an overall dataset of higher dimensionality.
	This motivates a need for statistical methods capable of making inferences
	regarding Boolean functions under conditions of noise and sparsity.
	RESULTS: We put forward a statistical model for sparse, noisy Boolean
	functions and methods for inference under the model. We focus on
	the case in which the form of the underlying Boolean function, as
	well as the number and identity of its inputs are all unknown. We
	present results on synthetic data and on a study of signalling proteins
	in cancer biology.},
  doi = {10.1093/bioinformatics/btn611},
  institution = {Department of Statistics, University of Warwick, Coventry, UK. s.n.mukherjee@warwick.ac.uk},
  owner = {holger},
  pii = {btn611},
  pmid = {19038985},
  timestamp = {2009.01.18},
  url = {http://dx.doi.org/10.1093/bioinformatics/btn611}
}

@ARTICLE{Mulder2008InterPro,
  author = {Nicola J. Mulder and Rolf Apweiler and Teresa K. Attwood and Amos
	Bairoch and Alex Bateman and David Binns and Peer Bork and Virginie
	Buillard and Lorenzo Cerutti and Richard Copley and Emmanuel Courcelle
	and Ujjwal Das and Louise Daugherty and Mark Dibley and Robert Finn
	and Wolfgang Fleischmann and Julian Gough and Daniel Haft and Nicolas
	Hulo and Sarah Hunter and Daniel Kahn and Alexander Kanapin and Anish
	Kejariwal and Alberto Labarga and Petra S. Langendijk-Genevaux and
	David Lonsdale and Rodrigo Lopez and Ivica Letunic and Martin Madera
	and John Maslen and Craig McAnulla and Jennifer McDowall and Jaina
	Mistry and Alex Mitchell and Anastasia N. Nikolskaya and Sandra Orchard
	and Christine Orengoa nd Robert Petryszak and Jeremy D. Selengut
	and Christian J. A. Sigrist and Paul D. Thomas and Franck Valentina
	nd Derek Wilson and Cathy H. Wu and Corin Yeats},
  title = {{New developments in the InterPro database}},
  journal = {Nucleic Acids Res.},
  year = {2008},
  volume = {35},
  pages = {D224 - D228},
  owner = {froehlih},
  timestamp = {2008.03.17}
}

@CONFERENCE{Muslea02SemiActive,
  author = {I. Muslea and S. Minton and C. Knoblock},
  title = {Active + semi-supervised learning = robust multi-view learning},
  booktitle = {Proc. 19th Int. Conf. Machine Learning},
  year = {2002},
  pages = {435 - 442}
}

@CONFERENCE{NadBen00,
  author = {C. Nadeau and Y. Bengio},
  title = {{Inference for the Generalization Error}},
  booktitle = {Adv. Neural Inf. Proc. Syst. 12},
  year = {2000},
  editor = {S. Solla and T. Leen and K.-R. M\"uller},
  address = {Cambridge, MA},
  publisher = {MIT Press}
}

@ARTICLE{Nahta2004,
  author = {Rita Nahta and Takeshi Takahashi and Naoto T Ueno and Mien-Chie Hung
	and Francisco J Esteva},
  title = {P27(kip1) down-regulation is associated with trastuzumab resistance
	in breast cancer cells.},
  journal = {Cancer Res},
  year = {2004},
  volume = {64},
  pages = {3981--3986},
  number = {11},
  month = {Jun},
  abstract = {Trastuzumab (Herceptin) is a recombinant humanized monoclonal antibody
	directed against HER-2. The objective response rate to trastuzumab
	monotherapy is 12-34\% for a median duration of 9 months, by which
	point most patients become resistant to treatment. We created two
	trastuzumab-resistant (TR) pools from the SKBR3 HER-2-overexpressing
	breast cancer cell line to study the mechanisms by which breast cancer
	cells escape trastuzumab-mediated growth inhibition. Both pools maintained
	her-2 gene amplification and protein overexpression. Resistant cells
	demonstrated a higher S-phase fraction by flow cytometry and a faster
	doubling time of 24-36 h compared with 72 h for parental cells. The
	cyclin-dependent kinase inhibitor p27(kip1) was decreased in TR cells,
	and cyclin-dependent kinase 2 activity was increased. Importantly,
	exogenous addition of p27(kip1) increased trastuzumab sensitivity.
	Additionally, resistant cells displayed heightened sensitivity to
	the proteasome inhibitor MG132, which induced p27(kip1) expression.
	Thus, we propose that trastuzumab resistance may be associated with
	decreased p27(kip1) levels and may be susceptible to treatments that
	induce p27(kip1) expression.},
  doi = {10.1158/0008-5472.CAN-03-3900},
  institution = {Department of Breast Medical Oncology, The University of Texas MD
	Anderson Cancer Center, Houston, Texas 77030-4009, USA.},
  keywords = {Antibodies, Monoclonal; Antineoplastic Agents; Breast Neoplasms; CDC2-CDC28
	Kinases; Cell Cycle Proteins; Cell Division; Cell Line, Tumor; Cyclin-Dependent
	Kinase 2; Cyclin-Dependent Kinase Inhibitor p27; Down-Regulation;
	Drug Resistance, Neoplasm; Gene Expression Regulation, Neoplastic;
	Humans; Tumor Suppressor Proteins},
  owner = {holger},
  pii = {64/11/3981},
  pmid = {15173011},
  timestamp = {2008.11.01},
  url = {http://dx.doi.org/10.1158/0008-5472.CAN-03-3900}
}

@ARTICLE{Nelander2008,
  author = {Sven Nelander and Weiqing Wang and Bj�rn Nilsson and Qing-Bai She
	and Christine Pratilas and Neal Rosen and Peter Gennemark and Chris
	Sander},
  title = {Models from experiments: combinatorial drug perturbations of cancer
	cells.},
  journal = {Mol Syst Biol},
  year = {2008},
  volume = {4},
  pages = {216},
  abstract = {We present a novel method for deriving network models from molecular
	profiles of perturbed cellular systems. The network models aim to
	predict quantitative outcomes of combinatorial perturbations, such
	as drug pair treatments or multiple genetic alterations. Mathematically,
	we represent the system by a set of nodes, representing molecular
	concentrations or cellular processes, a perturbation vector and an
	interaction matrix. After perturbation, the system evolves in time
	according to differential equations with built-in nonlinearity, similar
	to Hopfield networks, capable of representing epistasis and saturation
	effects. For a particular set of experiments, we derive the interaction
	matrix by minimizing a composite error function, aiming at accuracy
	of prediction and simplicity of network structure. To evaluate the
	predictive potential of the method, we performed 21 drug pair treatment
	experiments in a human breast cancer cell line (MCF7) with observation
	of phospho-proteins and cell cycle markers. The best derived network
	model rediscovered known interactions and contained interesting predictions.
	Possible applications include the discovery of regulatory interactions,
	the design of targeted combination therapies and the engineering
	of molecular biological networks.},
  doi = {10.1038/msb.2008.53},
  institution = {Computational Biology center, Memorial Sloan-Kettering Cancer Center,
	New York, NY, USA. multiple-perturbation@cbio.mskcc.org},
  keywords = {Breast Neoplasms; Cell Cycle; Cell Line, Tumor; Female; Humans; Models,
	Theoretical; Pharmaceutical Preparations; Phosphoproteins; Systems
	Biology},
  owner = {holger},
  pii = {msb200853},
  pmid = {18766176},
  timestamp = {2009.01.18},
  url = {http://dx.doi.org/10.1038/msb.2008.53}
}

@CONFERENCE{ng01spectral,
  author = {A. Ng and M. Jordan and Y. Weiss},
  title = {On spectral clustering: Analysis and an algorithm},
  booktitle = {Adv. Neural Inf. Proc. Syst. 14},
  year = {2002}
}

@ARTICLE{nieselt97,
  author = {Nieselt-Struwe, Katja},
  title = {Graphs in Sequence Spaces: a Review of Statistical Geometry},
  journal = {Biophysical Chemistry},
  year = {1997},
  volume = {66},
  pages = {111-131}
}

@ARTICLE{Nik03,
  author = {N. Nikolova and J. Jaworska},
  title = {{Approaches to Measure Chemical Similarity - a Review}},
  journal = {QSAR \& Combinatorial Science},
  year = {2003},
  volume = {22},
  number = {9-10}
}

@INCOLLECTION{Waterbeemd03BookSolub,
  author = {U. Norinder and M. Haeberlein},
  title = {Calculated Molecular Properties and Multivariate Statistical Analysis
	in Absorption Prediction},
  booktitle = {Drug Bioavailability},
  publisher = {Wiley-VCH},
  year = {2003},
  editor = {H. van de Waterbeemd and H. Lennern\"as and P. Artursson},
  pages = {358 - 405},
  address = {Weinheim}
}

@CONFERENCE{Ong04IndefKernels,
  author = {C. Soon Ong and X. Mary and S. Canu and A. Smola},
  title = {{Learning with Non-Positive Kernels}},
  booktitle = {Proc. Int. Conf. Machine Learning},
  year = {2004}
}

@ARTICLE{OpreaPharmaMapping02,
  author = {T. I. Oprea and I. Zamora and A.--L. Ungell},
  title = {Pharmacokinetically Based Mapping Device for Chemical Space Navigation},
  journal = {J. Comb. Chem.},
  year = {2002},
  volume = {4},
  pages = {258--266}
}

@ARTICLE{PalmHIA97,
  author = {K. Palm and P. Stenburg and K. Luthman and P. Artursson},
  title = {Polar Molecular Surface Properties Predict the Intestinal Absorption
	of Drugs in Humans},
  journal = {Pharm. Res.},
  year = {1997},
  volume = {14},
  pages = {586 - 571}
}

@ARTICLE{Park2005,
  author = {Kyeongmee Park and Keumhee Kwak and Jungyeon Kim and Sungjig Lim
	and Sehwan Han},
  title = {c-myc amplification is associated with HER2 amplification and closely
	linked with cell proliferation in tissue microarray of nonselected
	breast cancers.},
  journal = {Hum Pathol},
  year = {2005},
  volume = {36},
  pages = {634--639},
  number = {6},
  month = {Jun},
  abstract = {C-myc and HER2 amplification were analyzed on 214 consecutive breast
	cancers by fluorescence in situ hybridization using tissue microarray
	technology. The frequencies of amplification were 15.4\% (33/214)
	and 23.3\% (49/210), respectively. c- myc amplification was significantly
	associated with HER2 amplification ( P < .001) and closely linked
	with cell proliferative activity, measured by Ki67 labeling index
	( P = .010). In univariate survival analysis, lymph node status,
	tumor size, and histological grade were significant prognostic factors,
	but in multivariate analysis, lymph node status was the only significant
	factor. Patient survival did not differ according to c- myc amplification
	status, and c- myc amplification showed no significant correlation
	with clinicopathologic features of the tumors. A strong correlation
	between c- myc and HER2 amplification and proliferative activity
	indicates a biological link between these genes in breast cancer
	cell.},
  doi = {10.1016/j.humpath.2005.04.016},
  institution = {Department of Pathology, Inje University Sanggye Paik Hospital, Seoul,
	South Korea.},
  keywords = {Adult; Breast Neoplasms; Cell Proliferation; Disease-Free Survival;
	Female; Gene Amplification; Humans; Immunohistochemistry; In Situ
	Hybridization, Fluorescence; Lymphatic Metastasis; Middle Aged; Prognosis;
	Proto-Oncogene Proteins c-myc; Receptor, erbB-2; Survival Analysis},
  owner = {holger},
  pii = {S0046817705002303},
  pmid = {16021569},
  timestamp = {2008.11.01},
  url = {http://dx.doi.org/10.1016/j.humpath.2005.04.016}
}

@CONFERENCE{PavWes01,
  author = {P. Pavlidis and J. Weston and J. Cai and W. Grundy},
  title = {Gene functional classification from heteregoneous data},
  booktitle = {Proc. 5th Int. Conf. Computational Molecular Biology},
  year = {2001},
  pages = {242 - 248}
}

@ARTICLE{Peer2001BayesNet,
  author = {Pe'er, D. and Regev, A. and Elidan, G. and Friedman, N.},
  title = {Inferring subnetworks from perturbed expression profiles},
  journal = {Bioinformatics},
  year = {2001},
  volume = {17},
  pages = {S215 - S224},
  number = {Suppl 1},
  owner = {froehlih},
  timestamp = {2008.02.18}
}

@ARTICLE{Peer06Minreg,
  author = {D. Pe'er and A. Tanay and A. Regev},
  title = {{MinReg: A Scalable Algorithm for Learning Parsimonious Regulatory
	Networks in Yeast and Mammals}},
  journal = {J. Machine Learning Research},
  year = {2006},
  volume = {7},
  pages = {167 - 189}
}

@BOOK{Pearl2000Book,
  title = {Causality: Models, Reasoning and Inference},
  publisher = {Cambridge University Press},
  year = {2000},
  author = {J. Pearl},
  address = {Cambridge},
  owner = {froehlih},
  timestamp = {2008.02.18}
}

@CONFERENCE{Pearl1985,
  author = {J. Pearl},
  title = {Bayesian Networks: A Model of Self-Activated Memory for Evidential
	Reasoning},
  booktitle = {Proceedings of the 7th Conference of the Cognitive Science Society},
  year = {1985},
  pages = {329-334},
  owner = {froehlih},
  timestamp = {2008.10.02}
}

@ARTICLE{Pennisi2007GeneCount,
  author = {Elizabeth Pennisi},
  title = {Genetics. Working the (gene count) numbers: finally, a firm answer?},
  journal = {Science},
  year = {2007},
  volume = {316},
  pages = {1113},
  number = {5828},
  month = {May},
  doi = {10.1126/science.316.5828.1113a},
  keywords = {Genome, Human; Humans; Open Reading Frames},
  owner = {froehlih},
  pii = {316/5828/1113a},
  pmid = {17525311},
  timestamp = {2008.04.23},
  url = {http://dx.doi.org/10.1126/science.316.5828.1113a}
}

@BOOK{PiessensNumericIntegration1983,
  title = {Quadpack: a Subroutine Package for Automatic Integration},
  publisher = {Springer},
  year = {1983},
  author = {R. Piessens and E. deDoncker-Kapenga and C. Uberhuber and D. Kahaner},
  owner = {froehlih},
  timestamp = {2007.04.13}
}

@ARTICLE{Pors77FST,
  author = {R. Porsolt and M. Le Pichon and M. Jalfre},
  title = {A new animal model sensitive to antidepressant treatments},
  journal = {Nature},
  year = {1977},
  volume = {266},
  pages = {730 - 732}
}

@ARTICLE{Pounds2003localFDR,
  author = {S. Pounds and S. Morris},
  title = {Estimating the occurence of false positives and false negatives in
	microarray studies by approximating and partitioning the empirical
	distribution of p-values},
  journal = {Bioinformatics},
  year = {2003},
  volume = {19},
  pages = {1236 - 1242},
  number = {10},
  owner = {froehlih},
  timestamp = {2007.02.02}
}

@TECHREPORT{Poutre1987TransRed,
  author = {J. La Poutre and J. van Leeuwen},
  title = {Maintenance of Transitive Closures and Transitive Reductions of Graphs},
  institution = {Rijksuniversiteit Utrecht},
  year = {1987},
  number = {RUU-CS-87-25},
  owner = {froehlih},
  timestamp = {2006.11.30}
}

@ARTICLE{Pozo2008GOFuncSim,
  author = {Angela del Pozo and Florencio Pazos and Alfonso Valencia},
  title = {Defining functional distances over gene ontology.},
  journal = {BMC Bioinformatics},
  year = {2008},
  volume = {9},
  pages = {50},
  abstract = {BACKGROUND: A fundamental problem when trying to define the functional
	relationships between proteins is the difficulty in quantifying functional
	similarities, even when well-structured ontologies exist regarding
	the activity of proteins (i.e. 'gene ontology' -GO-). However, functional
	metrics can overcome the problems in the comparing and evaluating
	functional assignments and predictions. As a reference of proximity,
	previous approaches to compare GO terms considered linkage in terms
	of ontology weighted by a probability distribution that balances
	the non-uniform 'richness' of different parts of the Direct Acyclic
	Graph. Here, we have followed a different approach to quantify functional
	similarities between GO terms. RESULTS: We propose a new method to
	derive 'functional distances' between GO terms that is based on the
	simultaneous occurrence of terms in the same set of Interpro entries,
	instead of relying on the structure of the GO. The coincidence of
	GO terms reveals natural biological links between the GO functions
	and defines a distance model Df which fulfils the properties of a
	Metric Space. The distances obtained in this way can be represented
	as a hierarchical 'Functional Tree'. CONCLUSION: The method proposed
	provides a new definition of distance that enables the similarity
	between GO terms to be quantified. Additionally, the 'Functional
	Tree' defines groups with biological meaning enhancing its utility
	for protein function comparison and prediction. Finally, this approach
	could be for function-based protein searches in databases, and for
	analysing the gene clusters produced by DNA array experiments.},
  doi = {10.1186/1471-2105-9-50},
  keywords = {Algorithms; Amino Acid Sequence; Molecular Sequence Data; Multigene
	Family; Proteins; Sequence Alignment; Sequence Analysis, Protein},
  owner = {froehlih},
  pii = {1471-2105-9-50},
  pmid = {18221506},
  timestamp = {2008.08.21},
  url = {http://dx.doi.org/10.1186/1471-2105-9-50}
}

@ARTICLE{prim57,
  author = {Prim, R.C.},
  title = {Shortest connection networks and some generalizations},
  journal = {Bell Sys. Tech. Journal},
  year = {1957},
  pages = {1389-1401},
  groupsearch = {0},
  keywords = {minimum spanning trees}
}

@ARTICLE{Proschak2007,
  author = {Ewgenij Proschak and J�rg K Wegner and Andreas Sch�ller and Gisbert
	Schneider and Uli Fechner},
  title = {Molecular query language (MQL)--a context-free grammar for substructure
	matching.},
  journal = {J Chem Inf Model},
  year = {2007},
  volume = {47},
  pages = {295--301},
  number = {2},
  abstract = {We have developed a Java library for substructure matching that features
	easy-to-read syntax and extensibility. This molecular query language
	(MQL) is grounded on a context-free grammar, which allows for straightforward
	modification and extension. The formal description of MQL is provided
	in this paper. Molecule primitives are atoms, bonds, properties,
	branching, and rings. User-defined features can be added via a Java
	interface. In MQL, molecules are represented as graphs. Substructure
	matching was implemented using the Ullmann algorithm because of favorable
	run-time performance. The Ullmann algorithm carries out a fast subgraph
	isomorphism search by combining backtracking with effective forward
	checking. MQL software design was driven by the aim to facilitate
	the use of various cheminformatics toolkits. Two Java interfaces
	provide a bridge from our MQL package to an external toolkit: the
	first one provides the matching rules for every feature of a particular
	toolkit; the second one converts the found match from the internal
	format of MQL to the format of the external toolkit. We already implemented
	these interfaces for the Chemistry Development Toolkit.},
  doi = {10.1021/ci600305h},
  institution = {Johann Wolfgang Goethe-University, Institute of Organic Chemistry
	and Chemical Biology, Chair for Chem- and Bioinformatics, Siesmayerstrasse
	70, D-60323 Frankfurt am Main, Germany. proschak@bioinformatik.uni-frankfurt.de},
  keywords = {Algorithms; Computer Simulation; Models, Molecular; Molecular Structure;
	Pharmaceutical Preparations; Software Design},
  owner = {froehlih},
  pmid = {17381167},
  timestamp = {2008.12.12},
  url = {http://dx.doi.org/10.1021/ci600305h}
}

@ARTICLE{Pruitt2007RefSeq,
  author = {Kim D Pruitt and Tatiana Tatusova and Donna R Maglott},
  title = {NCBI reference sequences (RefSeq): a curated non-redundant sequence
	database of genomes, transcripts and proteins.},
  journal = {Nucleic Acids Res},
  year = {2007},
  volume = {35},
  pages = {D61--D65},
  number = {Database issue},
  month = {Jan},
  abstract = {NCBI's reference sequence (RefSeq) database (http://www.ncbi.nlm.nih.gov/RefSeq/)
	is a curated non-redundant collection of sequences representing genomes,
	transcripts and proteins. The database includes 3774 organisms spanning
	prokaryotes, eukaryotes and viruses, and has records for 2,879,860
	proteins (RefSeq release 19). RefSeq records integrate information
	from multiple sources, when additional data are available from those
	sources and therefore represent a current description of the sequence
	and its features. Annotations include coding regions, conserved domains,
	tRNAs, sequence tagged sites (STS), variation, references, gene and
	protein product names, and database cross-references. Sequence is
	reviewed and features are added using a combined approach of collaboration
	and other input from the scientific community, prediction, propagation
	from GenBank and curation by NCBI staff. The format of all RefSeq
	records is validated, and an increasing number of tests are being
	applied to evaluate the quality of sequence and annotation, especially
	in the context of complete genomic sequence.},
  doi = {10.1093/nar/gkl842},
  keywords = {Amino Acid Sequence; Base Sequence; Databases, Nucleic Acid; Databases,
	Protein; Genome; Internet; National Library of Medicine (U.S.); Quality
	Control; RNA, Messenger; Reference Standards; Sequence Analysis,
	DNA; Sequence Analysis, Protein; Sequence Analysis, RNA; United States;
	User-Computer Interface},
  owner = {froehlih},
  pii = {gkl842},
  pmid = {17130148},
  timestamp = {2008.04.22},
  url = {http://dx.doi.org/10.1093/nar/gkl842}
}

@ARTICLE{quackenbush01,
  author = {Quackenbush, J.},
  title = {Computational analysis of microarray data},
  journal = {Nature Reviews Genetics},
  year = {2001},
  volume = {2},
  pages = {418-427},
  groupsearch = {0},
  keywords = {reviews}
}

@ARTICLE{QuinlanDecTree86,
  author = {J. Quinlan},
  title = {Introduction of decision trees},
  journal = {Machine Learning},
  year = {1986},
  volume = {1},
  pages = {81 - 106}
}

@ARTICLE{Irizarry2003,
  author = {Irizarry RA and Bolstad BM and Collin F and Cope LM and Hobbs B and
	Speed TP},
  title = {Summaries of Affymetrix GeneChip probe level data},
  journal = {Nucleic Acids Res.},
  year = {2003},
  volume = {31},
  pages = {e15},
  number = {4},
  owner = {froehlih},
  timestamp = {2007.09.24}
}

@ARTICLE{Rada89,
  author = {Rada, R. and Mili, H. and Bicknell, E. and Bletner, M.},
  title = {Development and Application of a Metric on Semantic Nets},
  journal = {IEEE Transactions on Systems, Man, and Cybernetics},
  year = {1989},
  volume = {19(1)},
  pages = {17-30},
  groupsearch = {0},
  keywords = {semantic distances}
}

@CONFERENCE{DeRaedt01,
  author = {L. De Raedt and S. Kramer},
  title = {The levelwise version space algorithm and its application to molecular
	fragment finding},
  booktitle = {Proc. 17th Int. Conf. on AI},
  year = {2001},
  pages = {853 - 862},
  publisher = {Morgan Kaufmann}
}

@CONFERENCE{KramerDeRaedt01,
  author = {L. De Raedt and S. Kramer},
  title = {Feature Construction with version spaces for biochemical application},
  booktitle = {Proc. 18th Int. Conf. on Machine Learning},
  year = {2001},
  pages = {258 - 265}
}

@ARTICLE{RakFeatSelSVM03,
  author = {A. Rakotomamonjy},
  title = {Variable Selection Using SVM based Criteria},
  journal = {J. Machine Learning Research: Special Issue on Variable and Feature
	Selection},
  year = {2003},
  volume = {3},
  pages = {1357 - 1370}
}

@ARTICLE{rand71,
  author = {Rand, W. M.},
  title = {Objective criteria for the evaluation of clustering methods.},
  journal = {Journal of the American Statistical Association},
  year = {1971},
  volume = {66},
  pages = {846-850}
}

@ARTICLE{RareyFeatureTrees98,
  author = {M. Rarey and S. Dixon},
  title = {Feature trees: A new molecular similarity measure based on tree-matching},
  journal = {J. Computer-Aided Molecular Design},
  year = {1998},
  volume = {12},
  pages = {471 - 490}
}

@ARTICLE{raychaudhuri03,
  author = {Raychaudhuri, S. and Altman, R.B.},
  title = {A literature-based method for assessing the functional coherence
	of a gene group},
  journal = {Bioinformatics},
  year = {2003},
  volume = {19},
  pages = {396-401},
  number = {3},
  doi = {10.1093/bioinformatics/btg002}
}

@CONFERENCE{Ray00,
  author = {M. Raymer and W. Punch and E. Goodman and L. Kuhn and A. Jain},
  title = {Dimensionality Reduction Using Genetic Algorithms},
  booktitle = {IEEE Transactions on Evolutionary Computing},
  year = {2000}
}

@ARTICLE{RaymondGraphSimilarity02,
  author = {J. Raymond and E. Gardiner and P. Willett and P. Rascal},
  title = {Calculation of Graph Similarity using Maximum Common Edge Subgraphs},
  journal = {The Computer Journal},
  year = {2002},
  volume = {45},
  pages = {631 - 644},
  number = {6}
}

@CONFERENCE{Renyi61,
  author = {A. Renyi},
  title = {On measures of entropy and information},
  booktitle = {Proc. 4th Berkely Symp. on Mathematical Statistics and Probability},
  year = {1961},
  pages = {547 - 561}
}

@ARTICLE{Resnik99,
  author = {Resnik, P.},
  title = {Semantic Similarity in a Taxonomy: An information-based measure and
	its application to problems of ambigiguity in natural language},
  journal = {Journal of Artificial Intelligence Research},
  year = {1999},
  volume = {11},
  pages = {95-130},
  address = {Montreal},
  groupsearch = {0},
  keywords = {semantic distances}
}

@INPROCEEDINGS{Resnik95,
  author = {Resnik, P.},
  title = {Using Information Content to Evaluate Semantic Similarity in a Taxonomy},
  booktitle = {Proceedings of the 14th International Joint Conference on Artificial
	Intelligence},
  year = {1995},
  volume = {1},
  pages = {448-453},
  address = {Montreal},
  groupsearch = {0},
  keywords = {semantic distances}
}

@CONFERENCE{KellyEditDistanceGraph03,
  author = {A. Robes-Kelly and E. Hancock},
  title = {{Edit Distance From Graph Spectra}},
  booktitle = {Proc. 9th IEEE Int. Conf. Comp. Vis.},
  year = {2003},
  volume = {1},
  pages = {234 - 241}
}

@ARTICLE{robinson03,
  author = {Robinson, P.N and Wollstein, A. and B?hme U. and Beattie B.},
  title = {Ontologizing gene-expression microarray data: characterizing clusters
	with Gene Ontology},
  journal = {Bioinformatics},
  year = {2003},
  volume = {20},
  pages = {979-981},
  number = {6},
  doi = {10.1093/bioinformatics/bth040}
}

@ARTICLE{Rogers2005DependencyNetworks,
  author = {S. Rogers and M. Girolami},
  title = {A Bayesian regression approach to the inference of regulatory networks
	from gene expression data},
  journal = {Bioinformatics},
  year = {2005},
  volume = {21},
  pages = {3131 - 3137},
  number = {14},
  owner = {froehlih},
  timestamp = {2008.02.18}
}

@CONFERENCE{rosales03learning,
  author = {R. Rosales and B. Frey},
  title = {Learning generative models of affinity matrices},
  booktitle = {19th Conf. on Uncertainty in Artificial Intelligence (UAI)},
  year = {2003},
  volume = {8}
}

@ARTICLE{Rosenblatt58,
  author = {F. Rosenblatt},
  title = {{The Perceptron: a Probalistic Model for Information Storage and
	Organization in the Brain}},
  journal = {Psychol. Review},
  year = {1958},
  volume = {65},
  pages = {386 - 408}
}

@ARTICLE{RosipalKPLS01,
  author = {R. Rosipal and L. Trejo},
  title = {Kernel Partial Least Squares Regression in Reproducing Kernel Hilbert
	Space},
  journal = {J. Machine Learning Research},
  year = {2001},
  volume = {2},
  pages = {97 - 123}
}

@CONFERENCE{roth-resampling,
  author = {Volker Roth and Tilman Lange and Mikio Braun and Joachim Buhmann},
  title = {A Resampling Approach to Cluster Validation},
  booktitle = {Proc. Comput. Statistics: 15th Symposium Held in Berlin},
  year = {2002},
  pages = {123 - 128},
  url = {citeseer.ist.psu.edu/article/roth02resampling.html}
}

@ARTICLE{rousseeuw87,
  author = {Rousseeuw, P.J.},
  title = {Silhouettes: a graphical aid to the interpretation and validation
	of cluster analysis},
  journal = {J. Comp. and Applied Mathematics},
  year = {1987},
  volume = {20},
  pages = {53-65}
}

@ARTICLE{Rung2002DisruptionNetworks,
  author = {Rung, J. and Schlitt, T. and Brazma, A. and Freivalds, K. and Vilo,
	J.},
  title = {Building and analysing genome-wide gene disruption networks},
  journal = {Bioinformatics},
  year = {2002},
  volume = {18},
  pages = {S202 - S210},
  number = {Suppl 2},
  owner = {froehlih},
  timestamp = {2008.02.18}
}

@BOOK{RusNor95,
  title = {Artificial Intelligence - A Modern Approach},
  publisher = {Prentice Hall Inc.},
  year = {1995},
  author = {S. Russel and P. Norvig},
  address = {New Jersey}
}

@ARTICLE{Sachs2005BayesNet,
  author = {K. Sachs and O. Perez and D. Pe'er and D. Lauffenburger and G. Nolan},
  title = {Causal protein-signaling networks derived from multiparameter single-cell
	data},
  journal = {Science},
  year = {2005},
  volume = {208},
  pages = {523 - 529},
  number = {5721},
  owner = {froehlih},
  timestamp = {2008.02.18}
}

@ARTICLE{Sahin2008BooleanNetwork,
  author = {\"O. Sahin and H. Fr\"ohlich and C. L\"oebke and U. Korf and S. Burmester
	and M. Majety and J. Mattern and I. Schupp and C. Chaouiya and D.
	Thieffry and A. Poustka and S. Wiemann and T. Bei{\ss}barth and D.
	Arlt},
  title = {Modeling ERBB receptor-regulated G1/S transition to find novel targets
	for de novo trastuzumab resistance},
  journal = {BMC Systems Biology},
  year = {2009},
  volume = {3},
  pages = {1},
  owner = {holger},
  timestamp = {2008.11.01}
}

@ARTICLE{Sahin2007,
  author = {Ozg\"ur Sahin and Christian L\"obke and Ulrike Korf and Heribert
	Appelhans and Holger S\"ultmann and Annemarie Poustka and Stefan
	Wiemann and Dorit Arlt},
  title = {Combinatorial RNAi for quantitative protein network analysis.},
  journal = {Proc Natl Acad Sci U S A},
  year = {2007},
  volume = {104},
  pages = {6579--6584},
  number = {16},
  month = {Apr},
  abstract = {The elucidation of cross-talk events between intersecting signaling
	pathways is one main challenge in biological research. The complexity
	of protein networks, composed of different pathways, requires novel
	strategies and techniques to reveal relevant interrelations. Here,
	we established a combinatorial RNAi strategy for systematic single,
	double, and triple knockdown, and we measured the residual mRNAs
	and proteins quantitatively by quantitative real-time PCR and reverse-phase
	protein arrays, respectively, as a prerequisite for data analysis.
	Our results show that the parallel knockdown of at least three different
	genes is feasible while keeping both untargeted silencing and cytotoxicity
	low. The technique was validated by investigating the interplay of
	tyrosine kinase receptor ErbB2 and its downstream targets Akt-1 and
	MEK1 in cell invasion. This experimental approach combines multiple
	gene knockdown with a subsequent quantitative validation of reduced
	protein expression and is a major advancement toward the analysis
	of signaling pathways in systems biology.},
  doi = {10.1073/pnas.0606827104},
  keywords = {Cell Line, Tumor; Combinatorial Chemistry Techniques; Humans; Proteins;
	RNA Interference; RNA, Small Interfering; Receptor Cross-Talk; Signal
	Transduction; Systems Biology},
  owner = {froehlih},
  pii = {0606827104},
  pmid = {17420474},
  timestamp = {2008.10.23},
  url = {http://dx.doi.org/10.1073/pnas.0606827104}
}

@CONFERENCE{SanCumCruz02,
  author = {S. Salcedo-Sanz and M. Prado-Cumplido and F. Perez-Cruz and C. Bousono-Calzon},
  title = {{Feature Selection via Genetic Optimization}},
  booktitle = {Proc. Int. Conf. Artifical Neural Networks 2002},
  year = {2002},
  pages = {547 - 552}
}

@CONFERENCE{SchBurVap95,
  author = {B. Sch\"olkopf and C. Burges and V. Vapnik},
  title = {Extracting support data for a given task},
  booktitle = {First Int. Conf. for Knowledge Discovery and Data Mining},
  year = {1995},
  editor = {U. N. Fayyad and R. Uthurusamy},
  address = {Menlo Park},
  publisher = {AAAI Press}
}

@ARTICLE{SchnuSVM00,
  author = {B. Sch\"olkopf and A. Smola and R. Williamson and P. Bartlett},
  title = {New support vector algorithms},
  journal = {Neural Computation},
  year = {2000},
  volume = {12},
  pages = {1207 - 1245}
}

@BOOK{SchSmo02,
  title = {{Learning with Kernels}},
  publisher = {MIT Press},
  year = {2002},
  author = {B. Sch\"olkopf and A. J. Smola},
  address = {Cambridge, MA}
}

@BOOK{SchTsudaBioInfBook04,
  title = {Kernel Methods in Computational Biology},
  publisher = {MIT Press},
  year = {2004},
  author = {B. Sch\"olkopf and K. Tsuda and J.-P. Vert},
  address = {Cambridge, MA}
}

@ARTICLE{Schlicker2006GOFuncSim,
  author = {Andreas Schlicker and Francisco S Domingues and J�rg Rahnenf�hrer
	and Thomas Lengauer},
  title = {{A new measure for functional similarity of gene products based on
	Gene Ontology}},
  journal = {BMC Bioinformatics},
  year = {2006},
  volume = {7},
  pages = {302},
  owner = {froehlih},
  timestamp = {2007.12.05}
}

@INPROCEEDINGS{Schohn00ActiveLearningSVM,
  author = {Greg Schohn and David Cohn},
  title = {Less is More: {A}ctive Learning with Support Vector Machines},
  booktitle = {Proc. 17th International Conf. on Machine Learning},
  year = {2000},
  pages = {839 - 846},
  publisher = {Morgan Kaufmann, San Francisco, CA},
  url = {citeseer.ist.psu.edu/schohn00less.html}
}

@BOOK{Sedgewick2002Book,
  title = {Algorithms},
  publisher = {Addison-Wesley},
  year = {2002},
  author = {R. Sedgewick},
  owner = {froehlih},
  timestamp = {2008.06.17}
}

@ARTICLE{shah04,
  author = {Shah, N.H. and Fedoroff, N.V.},
  title = {{CLENCH}: a program for calculating {C}luster {EN}ri{CH}ment using
	{G}ene {O}ntology},
  journal = {Bioinformatics},
  year = {2004},
  volume = {20},
  pages = {1196-1197},
  number = {7},
  doi = {10.1093/bioinformatics/bth056}
}

@BOOK{CrisTaylorKernelMethods04,
  title = {Kernel Methods for Pattern Analysis},
  publisher = {Cambridge University Press},
  year = {2004},
  author = {J. Shawe-Taylor and N. Cristianini},
  address = {Cambridge, UK}
}

@ARTICLE{shi00normalized,
  author = {J. Shi and J. Malik},
  title = {Normalized Cuts and Image Segmentation},
  journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
  year = {2000},
  volume = {22},
  pages = {888-905},
  number = {8}
}

@BOOK{Siek2002BoostGraphLibrary,
  title = {The Boost Graph Library: User Guide and Reference Manual},
  publisher = {Addison-Wesley, Pearson Education Inc.},
  year = {2002},
  author = {Jeremy G. Siek and Lie-Quan Lee and Andrew Lumsdaine},
  address = {Boston, MA, USA},
  owner = {froehlih},
  timestamp = {2008.04.01}
}

@TECHREPORT{SmolaSVRTut98,
  author = {A. Smola and B. Sch\"olkopf},
  title = {A Tutorial on Support Vector Regression},
  institution = {NeuroCOLT2 Technical Report Series},
  year = {1998},
  number = {NC2-TR-1998-030}
}

@ARTICLE{Smith2004Limma,
  author = {G. Smyth},
  title = {{Linear models and empirical Bayes methods for assessing differential
	expression in microarray experiments}},
  journal = {Statistical Applications in Genetics and Molecular Biology},
  year = {2004},
  volume = {3},
  number = {1},
  owner = {froehlih},
  timestamp = {2006.11.28}
}

@ARTICLE{sohler04,
  author = {Sohler, F. and Hanisch, D. and Zimmer, R.},
  title = {New methods for joint analysis of biological networks and expression
	data},
  journal = {Bioinformatics},
  year = {2004},
  volume = {20},
  pages = {1517-1521},
  number = {10},
  doi = {10.1093/bioinformatics/bth112}
}

@CONFERENCE{FroeGO05,
  author = {N. Speer and H. Fr\"ohlich and C. Spieth and A. Zell},
  title = {Functional Grouping of Genes Using Spectral Clustering and Gene Ontology},
  booktitle = {Proc. Int. Joint Conf. Neural Networks},
  year = {2005},
  pages = {298 - 303}
}

@INPROCEEDINGS{speer05GOSlim,
  author = {N. Speer and H. Fr{\"o}hlich and C. Spieth and A. Zell},
  title = {Functional Distances for Genes Based on GO Feature Maps and their
	Application to Clustering},
  booktitle = {Proc. IEEE Symp. on Comp. Intel. in Bioinf. and Comp. Biology (CIBCB
	2005) },
  year = {2005},
  pages = {142 - 149},
  address = {San Diego, USA},
  publisher = {IEEE Press}
}

@ARTICLE{Stossi2006ESR1CCNG2,
  author = {F. Stossi and V. Likhite and J. Katzenellenbogen and B. Katzenellenbogen},
  title = {Estrogen-occupied Estrogen Receptor Represses Cyclin G2 Gene Expression
	and Recruits a Repressor Complex and Cyclin G2 Promoter},
  journal = {The Journal of Biological Chemistry},
  year = {2006},
  volume = {281},
  pages = {16272 - 16287},
  number = {24},
  owner = {froehlih},
  timestamp = {2007.11.16}
}

@ARTICLE{SEDUMI99,
  author = {J. Sturm},
  title = {Using SeDuMi1.02 a MATLAB toolbox for optimization over symmetric
	cones},
  journal = {Optimization Methods and Software},
  year = {1999},
  volume = {11/12},
  pages = {625 - 653},
  number = {1 - 4}
}

@INPROCEEDINGS{syswerda89,
  author = {Syswerda, G.},
  title = {Uniform crossover in genetic algorithms},
  booktitle = {Proceedings of the 3rd International Conference on Genetic Algorithms},
  year = {1989},
  pages = {2-9},
  groupsearch = {0},
  keywords = {evolutionary algorithms}
}

@ARTICLE{Takamori2001FST,
  author = {Takamori, K. and Yoshida, S. and Okuyama, S.},
  title = {{Effect of ACTH on the imipramine- and desipramine-induced decrease
	in duration of immobility time as measured in a rat forced swimming
	test]}},
  journal = {Life Sci.},
  year = {2001},
  volume = {69},
  pages = {1891-1896},
  owner = {froehlih},
  timestamp = {2006.07.13}
}

@INPROCEEDINGS{tamayo99,
  author = {Tamayo, P. and Slonim, D. and Mesirov, J.and Zhu, Q. and Kitareewan,
	S. and Dmitrovsky, E. and Lander, E.S. and Golub, T.R.},
  title = {Interpreting Patterns of Gene Expression with Self-Organizing Maps:
	Methods and application to hematopoietic differentiation},
  booktitle = {Proceedings of the National Academy of Sciences, USA},
  year = {1999},
  volume = {96},
  pages = {2907-2912},
  groupsearch = {0},
  keywords = {mathematical clustering}
}

@ARTICLE{Tanno2001,
  author = {S. Tanno and S. Tanno and Y. Mitsuuchi and D. A. Altomare and G.
	H. Xiao and J. R. Testa},
  title = {AKT activation up-regulates insulin-like growth factor I receptor
	expression and promotes invasiveness of human pancreatic cancer cells.},
  journal = {Cancer Res},
  year = {2001},
  volume = {61},
  pages = {589--593},
  number = {2},
  month = {Jan},
  abstract = {Insulin-like growth factor I receptor (IGF-IR) is frequently overexpressed
	in several types of human malignancy and is associated with invasion
	and metastasis of tumor cells. Recently, IGF-IR expression was reported
	to be up-regulated in the human pancreatic cancer cell line PANC-1
	when cells were stably transfected with active Src. The downstream
	targets of Src that lead to the up-regulation of IGF-IR expression
	were previously unknown. We demonstrate here that AKT regulates IGF-IR
	expression in PANC-1 and AsPC-1 cells. Cells transfected with active
	Src exhibited significantly more IGF-IR protein compared with vector-transfected
	cells. Overexpression of wild-type or constitutively active AKT (i.e.,
	AKT1 or AKT2) also resulted in elevated IGF-IR expression. IGF-IR
	protein levels were higher in cells transfected with constitutively
	active AKT than in cells transfected with active Src. In vitro kinase
	assays showed that AKT kinases are activated by active Src and inhibited
	by dominant negative Src or the tumor suppressor PTEN. Furthermore,
	AKT-induced IGF-IR expression was down-regulated by dominant-negative
	Src or PTEN. In addition, cells transfected with activated AKT in
	the presence of IGF-I were shown to have enhanced invasiveness compared
	with control cells. These data provide evidence for a link between
	AKT signaling and the regulation of IGF-IR expression and demonstrate
	that active AKT promotes the invasiveness of pancreatic cancer cells
	through the up-regulation of IGF-IR expression.},
  institution = {Human Genetics Program, Fox Chase Cancer Center, Philadelphia, Pennsylvania
	19111, USA.},
  keywords = {Blotting, Western; DNA, Recombinant; Down-Regulation; Enzyme Activation;
	Humans; Neoplasm Invasiveness; PTEN Phosphohydrolase; Pancreatic
	Neoplasms, genetics/metabolism/pathology; Phosphoric Monoester Hydrolases,
	metabolism; Plasmids, genetics; Precipitin Tests; Protein-Serine-Threonine
	Kinases; Proto-Oncogene Proteins c-akt; Proto-Oncogene Proteins,
	genetics/metabolism; Receptor, IGF Type 1, metabolism; Transfection;
	Tumor Cells, Cultured; Tumor Suppressor Proteins; Up-Regulation;
	src-Family Kinases, metabolism},
  owner = {holfro},
  pmid = {11212254},
  timestamp = {2009.02.20}
}

@ARTICLE{tavazoie99,
  author = {Tavazoie, S. and Hughes, J.D. and Campbell, M.J. and Cho, R.J. and
	Church, G.M.},
  title = {Systematic Determination of Genetic Network Architecture},
  journal = {Nature Genetics},
  year = {1999},
  volume = {22},
  pages = {281-285},
  groupsearch = {0},
  keywords = {reviews}
}

@CONFERENCE{TesmerAMIFS04,
  author = {M. Tesmer and P. Estevez},
  title = {AMIFS: Adaptive Feature Selection by Using Mutual Information},
  booktitle = {Proc. Int. Joint Conf. Neural Networks},
  year = {2004},
  volume = {1},
  pages = {303 - 308}
}

@MISC{GOConsortiumWeb,
  author = {{The Gene Ontology Consortium}},
  howpublished = {http://www.geneontology.org},
  groupsearch = {0},
  keywords = {gene ontology}
}

@MISC{Thea,
  author = {{The Gene Ontology Consortium}},
  howpublished = {http://www.geneontology.org},
  groupsearch = {0},
  keywords = {gene ontology},
  owner = {froehlih},
  timestamp = {2008.10.02}
}

@ARTICLE{GOConsortium04,
  author = {{The Gene Ontology Consortium}},
  title = {The Gene Ontology ({GO}) database and informatics resource},
  journal = {Nucleic Acids Research},
  year = {2004},
  volume = {32},
  pages = {D258-D261},
  groupsearch = {0},
  keywords = {gene ontology}
}

@ARTICLE{GOConsortium01,
  author = {{The Gene Ontology Consortium}},
  title = {Creating the gene ontology resource: design and implementation},
  journal = {Genome Research},
  year = {2001},
  volume = {11(8)},
  pages = {1425-1433},
  groupsearch = {0},
  keywords = {gene ontology}
}

@MISC{GOMySQL,
  author = {{The Gene Ontology Database}},
  howpublished = {\\http://www.godatabase.org/dev/database},
  groupsearch = {0},
  keywords = {gene ontology}
}

@MISC{The,
  author = {{The Gene Ontology Database}},
  howpublished = {\\http://www.godatabase.org/dev/database},
  groupsearch = {0},
  keywords = {gene ontology},
  owner = {froehlih},
  timestamp = {2008.10.02}
}

@ARTICLE{Tibes2006,
  author = {Raoul Tibes and Yihua Qiu and Yiling Lu and Bryan Hennessy and Michael
	Andreeff and Gordon B Mills and Steven M Kornblau},
  title = {Reverse phase protein array: validation of a novel proteomic technology
	and utility for analysis of primary leukemia specimens and hematopoietic
	stem cells.},
  journal = {Mol Cancer Ther},
  year = {2006},
  volume = {5},
  pages = {2512--2521},
  number = {10},
  month = {Oct},
  abstract = {Proteomics has the potential to provide answers in cancer pathogenesis
	and to direct targeted therapy through the comprehensive analysis
	of protein expression levels and activation status. The realization
	of this potential requires the development of new, rapid, high-throughput
	technologies for performing protein arrays on patient samples, as
	well as novel analytic techniques to interpret them. Herein, we describe
	the validation and robustness of using reverse phase protein arrays
	(RPPA) for the analysis of primary acute myelogenous leukemia samples
	as well as leukemic and normal stem cells. In this report, we show
	that array printing, detection, amplification, and staining precision
	are very high, reproducible, and that they correlate with traditional
	Western blotting. Using replicates of the same sample on the same
	and/or separate arrays, or using separate protein samples prepared
	from the same starting sample, the intra- and interarray reproducibility
	was extremely high. No statistically significant difference in protein
	signal intensities could be detected within the array setups. The
	activation status (phosphorylation) was maintained in experiments
	testing delayed processing and preparation from multiple freeze-thawed
	samples. Differences in protein expression could reliably be detected
	in as few as three cell protein equivalents. RPPA prepared from rare
	populations of normal and leukemic stem cells were successfully done
	and showed differences from bulk populations of cells. Examples show
	how RPPAs are ideally suited for the large-scale analysis of target
	identification, validation, and drug discovery. In summary, RPPA
	is a highly reliable, reproducible, high-throughput system that allows
	for the rapid large-scale proteomic analysis of protein expression
	and phosphorylation state in primary acute myelogenous leukemia cells,
	cell lines, and in human stem cells.},
  doi = {10.1158/1535-7163.MCT-06-0334},
  institution = {Department of Medical Oncology, The University of Texas M.D. Anderson
	Cancer Center, Houston, Texas 77030-4095, USA.},
  keywords = {Blotting, Western; Cell Line, Tumor; Hematopoietic Stem Cells; Humans;
	Leukemia, Myeloid, Acute; Prospective Studies; Protein Array Analysis;
	Proteome; Reproducibility of Results; Sensitivity and Specificity;
	Tumor Markers, Biological},
  owner = {holger},
  pii = {5/10/2512},
  pmid = {17041095},
  timestamp = {2009.01.18},
  url = {http://dx.doi.org/10.1158/1535-7163.MCT-06-0334}
}

@BOOK{TikhonovRegularization77,
  title = {Solutions of ill-posed problems},
  publisher = {W.H. Winston},
  year = {1977},
  author = {A. Tikhonov and V. Arsenin}
}

@ARTICLE{Tong01ActiveLearningSVM,
  author = {S. Tong and D. Koller},
  title = {Support Vector Machine Active Learning with Applications to Text
	Classification},
  journal = {J. Machine Learning Research},
  year = {2001},
  volume = {2},
  pages = {45 - 66}
}

@ARTICLE{Tresch2007TransRed,
  author = {Achim Tresch and T. Bei{\ss}barth and H. S\"ultmann and R. Kuner
	and A. Poustka and A. Buness},
  title = {{Discrimination of direct and indirect interactions in a network
	of regulatory effects}},
  journal = {J Comput Biol},
  year = {2007},
  volume = {14},
  pages = {1217 - 1228},
  number = {9},
  month = {Nov},
  abstract = {The matter of concern are algorithms for the discrimination of direct
	from indirect regulatory effects from an interaction graph built
	up by error-prone measurements. Many of these algorithms can be cast
	as a rule for the removal of a single edge of the graph, such that
	the remaining graph is still consistent with the data. A set of mild
	conditions is given under which iterated application of such a rule
	leads to a unique minimal consistent graph. We show that three of
	the common methods for direct interactions search fulfill these conditions,
	thus providing a justification of their use. The main issues a reconstruction
	algorithm has to deal with, are the noise in the data, the presence
	of regulatory cycles, and the direction of the regulatory effects.
	We introduce a novel rule that, in contrast to the previously mentioned
	methods, simultaneously takes into account all these aspects. An
	efficient algorithm for the computation of the minimal graph is given,
	whose time complexity is cubic in the number of vertices of the graph.
	Finally, we demonstrate the utility of our method in a simulation
	study.},
  doi = {10.1089/cmb.2007.0085},
  owner = {froehlih},
  pmid = {17990974},
  timestamp = {2008.03.18},
  url = {http://dx.doi.org/10.1089/cmb.2007.0085}
}

@ARTICLE{Tresch2008NEMs,
  author = {A. Tresch and F. Markowetz},
  title = {{Structure Learning in Nested Effects Models}},
  journal = {{Statistical Applications in Genetics and Molecular Biology}},
  year = {2008},
  volume = {7},
  number = {1},
  note = {in Press},
  owner = {froehlih},
  timestamp = {2008.02.04}
}

@ARTICLE{Tusher2001SAM,
  author = {V. Tusher and R. Tibshirani and G. Chu},
  title = {Significance analysis of microarrays applied to the ionizing radiation
	response},
  journal = {Proc. Nat. Acad. Sci.},
  year = {2001},
  volume = {98},
  pages = {5116-5121},
  owner = {froehlih},
  timestamp = {2008.03.17}
}

@INCOLLECTION{Vaf93,
  author = {H. Vafaie and K. De Jong},
  title = {Evolutionary feature space transformation},
  booktitle = {Feature Extraction, Construction and Selection: a data mining perspective},
  publisher = {Kluwer},
  year = {1998},
  editor = {H. Liu and H. Motoda},
  pages = {307 - 323}
}

@ARTICLE{VandenbergheSDP96,
  author = {L. Vandenberghe and S. Boyd},
  title = {Semidefinite Programming},
  journal = {SIAM Review},
  year = {1996},
  volume = {38},
  pages = {49 - 95},
  number = {1}
}

@BOOK{Vapnik98,
  title = {Statistical Learning Theory},
  publisher = {John Wiley and Sons},
  year = {1998},
  author = {V. Vapnik},
  address = {New York}
}

@BOOK{Vapnik95,
  title = {{The Nature of Statistical Learning Theory}},
  publisher = {Springer},
  year = {1995},
  author = {V. Vapnik},
  address = {New York, NY}
}

@BOOK{Vapnik79,
  title = {Estimation of Dependencies Based on Empirical Data (in Russian)},
  publisher = {Nauka},
  year = {1979},
  author = {V. Vapnik},
  address = {Moscow}
}

@ARTICLE{VapCha00,
  author = {V. Vapnik and O. Chapelle},
  title = {{Bounds on error expectation for Support Vector Machines}},
  journal = {Neural Computation},
  year = {2000},
  volume = {12},
  number = {9}
}

@INCOLLECTION{VapChapSpanBound00,
  author = {V. Vapnik and O. Chapelle},
  title = {Bounds on Error Expectation for SVM},
  booktitle = {Advances in Large Margin Classifiers},
  publisher = {MIT Press},
  year = {2000},
  pages = {261 - 280},
  address = {Cambridge, MA}
}

@ARTICLE{VapCherUniConv68,
  author = {V. Vapnik and A. Chervonenkis},
  title = {Uniform convergence of frequencies of occurences of events to their
	probabilities},
  journal = {Dokl. Akad. Nauk SSSR},
  year = {1968},
  volume = {181},
  pages = {915 - 918}
}

@ARTICLE{Vaske2009,
  author = {Charles J Vaske and Carrie House and Truong Luu and Bryan Frank and
	Chen-Hsiang Yeang and Norman H Lee and Joshua M Stuart},
  title = {A factor graph nested effects model to identify networks from genetic
	perturbations.},
  journal = {PLoS Comput Biol},
  year = {2009},
  volume = {5},
  pages = {e1000274},
  number = {1},
  month = {Jan},
  abstract = {Complex phenotypes such as the transformation of a normal population
	of cells into cancerous tissue result from a series of molecular
	triggers gone awry. We describe a method that searches for a genetic
	network consistent with expression changes observed under the knock-down
	of a set of genes that share a common role in the cell, such as a
	disease phenotype. The method extends the Nested Effects Model of
	Markowetz et al. (2005) by using a probabilistic factor graph to
	search for a network representing interactions among these silenced
	genes. The method also expands the network by attaching new genes
	at specific downstream points, providing candidates for subsequent
	perturbations to further characterize the pathway. We investigated
	an extension provided by the factor graph approach in which the model
	distinguishes between inhibitory and stimulatory interactions. We
	found that the extension yielded significant improvements in recovering
	the structure of simulated and Saccharomyces cerevisae networks.
	We applied the approach to discover a signaling network among genes
	involved in a human colon cancer cell invasiveness pathway. The method
	predicts several genes with new roles in the invasiveness process.
	We knocked down two genes identified by our approach and found that
	both knock-downs produce loss of invasive potential in a colon cancer
	cell line. Nested effects models may be a powerful tool for inferring
	regulatory connections and genes that operate in normal and disease-related
	processes.},
  doi = {10.1371/journal.pcbi.1000274},
  institution = {Biomolecular Engineering Department, University of California Santa
	Cruz, Santa Cruz, California, United States of America.},
  owner = {holfro},
  pmid = {19180177},
  timestamp = {2009.04.24},
  url = {http://dx.doi.org/10.1371/journal.pcbi.1000274}
}

@TECHREPORT{verma:03,
  author = {D. Verma and M. Meila},
  title = {A comparison of spectral clustering algorithms},
  institution = {University of Washington, CSE},
  year = {2003},
  number = {03-05-01}
}

@INCOLLECTION{SmolaStringTreeKernels04,
  author = {S. Vishwanathan and A. Smola},
  title = {{Fast Kernels for String and Tree Matching}},
  booktitle = {Kernel Methods in Computational Biology},
  publisher = {MIT Press},
  year = {2004},
  editor = {B. Sch\"olkopf and K. Tsuda and J.-P. Vert},
  pages = {113 - 130},
  address = {Cambridge, MA}
}

@ARTICLE{Vogel2001,
  author = {C. L. Vogel and M. A. Cobleigh and D. Tripathy and J. C. Gutheil
	and L. N. Harris and L. Fehrenbacher and D. J. Slamon and M. Murphy
	and W. F. Novotny and M. Burchmore and S. Shak and S. J. Stewart},
  title = {First-line Herceptin monotherapy in metastatic breast cancer.},
  journal = {Oncology},
  year = {2001},
  volume = {61 Suppl 2},
  pages = {37--42},
  abstract = {The pivotal phase II and III Herceptin trials proved the efficacy
	and safety of second- or third-line single-agent Herceptin and first-line
	Herceptin in combination with chemotherapy, respectively. In the
	current trial, 114 patients were randomized to one of two dose groups
	of first-line Herceptin monotherapy: standard dose of 4 mg/ kg initial
	dose followed by 2 mg/kg intravenous (i.v.) weekly; or high dose
	of 8 mg/kg initial dose followed by 4 mg/kg i.v. weekly. The regimen
	was generally well tolerated. A similar incidence of adverse events
	was demonstrated in the two dose groups with the possible exception
	of acute infusion-related events such as fever and chills as well
	as rash and dyspnea, which appear to be more prevalent in the higher
	dose group. The overall response rate was 26\% and response rates
	were similar between the two dose groups (24\% for the standard Herceptin
	dose group and 28\% for the high Herceptin dose group). Subgroup
	analysis determined a higher response rate in IHC 3+ patients (35\%)
	and FISH-positive patients (41\%). When women with stable disease
	for > or =6 months were included with responders, the clinical benefit
	rate in IHC 3+ patients was 47\%. Median survival was 24.4 months,
	which is comparable with the survival rate seen in the pivotal phase
	III combination trial (25 months). Therefore, single-agent Herceptin
	is an important new option for the first-line treatment of HER2-positive
	metastatic breast cancer patients.},
  keywords = {Antibodies, Monoclonal; Antineoplastic Agents; Breast Neoplasms; Clinical
	Trials, Phase II as Topic; Clinical Trials, Phase III as Topic; Combined
	Modality Therapy; Disease Progression; Disease-Free Survival; Female;
	Fever; Heart Diseases; Humans; Neoplasm Metastasis; Neoplasm Proteins;
	Pain; Palliative Care; Randomized Controlled Trials as Topic; Receptor,
	erbB-2; Receptors, Estrogen; Safety; Salvage Therapy; Survival Analysis;
	Treatment Outcome; Tumor Markers, Biological},
  owner = {froehlih},
  pii = {ocl1b037},
  pmid = {11694786},
  timestamp = {2008.10.23}
}

@ARTICLE{Vogel2002,
  author = {Charles L Vogel and Melody A Cobleigh and Debu Tripathy and John
	C Gutheil and Lyndsay N Harris and Louis Fehrenbacher and Dennis
	J Slamon and Maureen Murphy and William F Novotny and Michael Burchmore
	and Steven Shak and Stanford J Stewart and Michael Press},
  title = {Efficacy and safety of trastuzumab as a single agent in first-line
	treatment of HER2-overexpressing metastatic breast cancer.},
  journal = {J Clin Oncol},
  year = {2002},
  volume = {20},
  pages = {719--726},
  number = {3},
  month = {Feb},
  abstract = {PURPOSE: To evaluate the efficacy and safety of first-line, single-agent
	trastuzumab in women with HER2-overexpressing metastatic breast cancer.
	PATIENTS AND METHODS: One hundred fourteen women with HER2-overexpressing
	metastatic breast cancer were randomized to receive first-line treatment
	with trastuzumab 4 mg/kg loading dose, followed by 2 mg/kg weekly,
	or a higher 8 mg/kg loading dose, followed by 4 mg/kg weekly. RESULTS:
	The objective response rate was 26\% (95\% confidence interval [CI],
	18.2\% to 34.4\%), with seven complete and 23 partial responses.
	Response rates in 111 assessable patients with 3+ and 2+ HER2 overexpression
	by immunohistochemistry (IHC) were 35\% (95\% CI, 24.4\% to 44.7\%)
	and none (95\% CI, 0\% to 15.5\%), respectively. The clinical benefit
	rates in assessable patients with 3+ and 2+ HER2 overexpression were
	48\% and 7\%, respectively. The response rates in 108 assessable
	patients with and without HER2 gene amplification by fluorescence
	in situ hybridization (FISH) analysis were 34\% (95\% CI, 23.9\%
	to 45.7\%) and 7\% (95\% CI, 0.8\% to 22.8\%), respectively. Seventeen
	(57\%) of 30 patients with an objective response and 22 (51\%) of
	43 patients with clinical benefit had not experienced disease progression
	at follow-up at 12 months or later. The most common treatment-related
	adverse events were chills (25\% of patients), asthenia (23\%), fever
	(22\%), pain (18\%), and nausea (14\%). Cardiac dysfunction occurred
	in two patients (2\%); both had histories of cardiac disease and
	did not require additional intervention after discontinuation of
	trastuzumab. There was no clear evidence of a dose-response relationship
	for response, survival, or adverse events. CONCLUSION: Single-agent
	trastuzumab is active and well tolerated as first-line treatment
	of women with metastatic breast cancer with HER2 3+ overexpression
	by IHC or gene amplification by FISH.},
  keywords = {Adult; Aged; Aged, 80 and over; Antibodies, Monoclonal; Antineoplastic
	Agents; Breast Neoplasms; Female; Gene Amplification; Genes, erbB-2;
	Humans; Immunohistochemistry; In Situ Hybridization; Middle Aged;
	Quality of Life; Safety; Treatment Outcome},
  owner = {froehlih},
  pmid = {11821453},
  timestamp = {2008.10.23}
}

@ARTICLE{Wagner2002TransRed,
  author = {A. Wagner},
  title = {How to reconstruct a large genetic network from n gene perturbations
	in fewer than $n^2$ easy steps},
  journal = {Bioinformatics},
  year = {2001},
  volume = {17},
  pages = {1183 - 1197},
  number = {12},
  owner = {froehlih},
  timestamp = {2008.02.18}
}

@ARTICLE{Wang2008,
  author = {Li Wang and Ji Zhu and Hui Zou},
  title = {Hybrid huberized support vector machines for microarray classification
	and gene selection.},
  journal = {Bioinformatics},
  year = {2008},
  volume = {24},
  pages = {412--419},
  number = {3},
  month = {Feb},
  abstract = {MOTIVATION: The standard L(2)-norm support vector machine (SVM) is
	a widely used tool for microarray classification. Previous studies
	have demonstrated its superior performance in terms of classification
	accuracy. However, a major limitation of the SVM is that it cannot
	automatically select relevant genes for the classification. The L(1)-norm
	SVM is a variant of the standard L(2)-norm SVM, that constrains the
	L(1)-norm of the fitted coefficients. Due to the singularity of the
	L(1)-norm, the L(1)-norm SVM has the property of automatically selecting
	relevant genes. On the other hand, the L(1)-norm SVM has two drawbacks:
	(1) the number of selected genes is upper bounded by the size of
	the training data; (2) when there are several highly correlated genes,
	the L(1)-norm SVM tends to pick only a few of them, and remove the
	rest. RESULTS: We propose a hybrid huberized support vector machine
	(HHSVM). The HHSVM combines the huberized hinge loss function and
	the elastic-net penalty. By doing so, the HHSVM performs automatic
	gene selection in a way similar to the L(1)-norm SVM. In addition,
	the HHSVM encourages highly correlated genes to be selected (or removed)
	together. We also develop an efficient algorithm to compute the entire
	solution path of the HHSVM. Numerical results indicate that the HHSVM
	tends to provide better variable selection results than the L(1)-norm
	SVM, especially when variables are highly correlated. AVAILABILITY:
	R code are available at http://www.stat.lsa.umich.edu/~jizhu/code/hhsvm/.},
  doi = {10.1093/bioinformatics/btm579},
  keywords = {Algorithms; Artificial Intelligence; Gene Expression Profiling; Oligonucleotide
	Array Sequence Analysis; Pattern Recognition, Automated},
  owner = {froehlih},
  pii = {btm579},
  pmid = {18175770},
  timestamp = {2008.05.19},
  url = {http://dx.doi.org/10.1093/bioinformatics/btm579}
}

@ARTICLE{WangData97,
  author = {R. Wang and Y. Fu and L. Lai},
  title = {A New Atom-Additive Method for Calculating Partition Coefficients},
  journal = {J. Chem. Inf. Comp. Sci.},
  year = {1997},
  volume = {37},
  pages = {615 - 621}
}

@ARTICLE{WashioDataMining03,
  author = {T. Washio and H. Motoda},
  title = {State of the Art of Graph-based Data Mining},
  journal = {SIGKDD Explorations Special Issue on Multi-Relational Data Mining},
  year = {2003},
  volume = {5},
  issue = {1}
}

@ARTICLE{WaterbeemdADMET03,
  author = {H. van de Waterbeemd and E. Gifford},
  title = {{{ADMET} \textit{{I}n {S}ilico} {M}odelling: {T}owards {P}rediction
	{P}aradise}?},
  journal = {{Nature Reviews: Drug Discovery}},
  year = {2003},
  volume = {2},
  pages = {192--204},
  abstract = {Following studies in the late 1990s that indicated that poor pharmacokinetics
	and toxicity were important causes of costly late-stage failures
	in drug development, it has become widely appreciated that these
	areas should be considered as early as possible in the drug discovery
	process. However, in recent years, combinatorial chemistry and high-throughput
	screening have significantly increased the number of compounds for
	which early data on absorption, distribution, metabolism, excretion
	(ADME) and toxicity (T) are needed, which has in turn driven the
	develop-ment of a variety of medium and high-throughput in vitro
	ADMET screens. Here, we describe how in silico approaches will further
	increase our ability to predict and model the most relevant pharma-cokinetic,
	metabolic and toxicity endpoints, thereby accelerating the drug discovery
	process.},
  file = {wg03.pdf:wg03.pdf:PDF},
  groupsearch = {0}
}

@ARTICLE{WegFroeHSCS05,
  author = {J. Wegner and H. Fr\"ohlich and H. Mielenz and A. Zell},
  title = {Data and Graph Mining in Chemical Space for ADME and Activity Data
	Sets},
  journal = {QSAR \& Comb. Sci.},
  year = {2005},
  note = {to appear}
}

@ARTICLE{WegFroe03,
  author = {J. Wegner and H. Fr\"ohlich and A. Zell},
  title = {{Feature Selection for Descriptor based Classificiation Models: Part
	I - Theory and GA-SEC Algorithm}},
  journal = {J. Chem. Inf. Comput. Sci.},
  year = {2003},
  volume = {44},
  pages = {921 - 930}
}

@ARTICLE{WegFroe03:2,
  author = {J. Wegner and H. Fr\"ohlich and A. Zell},
  title = {{Feature selection for Descriptor based Classification Models: Part
	II - Human Intestinal Absorption (HIA)}},
  journal = {J. Chem. Inf. Comput. Sci.},
  year = {2003},
  volume = {44},
  pages = {931 - 939}
}

@ARTICLE{Wegner03,
  author = {J. Wegner and A. Zell},
  title = {{Prediction of Aqueous Solubility and Partition Coefficient Optimized
	by a Genetic Algorithm Based Descriptor Selection Method}},
  journal = {J. Chem. Inf. Comput. Sci.},
  year = {2003},
  volume = {43},
  pages = {1077 - 1084},
  number = {3}
}

@MISC{Wegner,
  author = {J???rg K. Wegner},
  title = {JOELIB - an Open Source chemoinformatics library},
  owner = {froehlih},
  timestamp = {2008.10.02},
  url = {http://sourceforge.net/projects/joelib/}
}

@PHDTHESIS{Wegner2006Diss,
  author = {J\"org K. Wegner},
  title = {{Data Mining und Graph Mining auf molekularen Graphen - Cheminformatik
	und molekulare Kodierungen f�r ADME/Tox \& QSAR-Analysen}},
  school = {Eberhard-Karls Universit\"at T\"ubingen},
  year = {2006},
  owner = {froehlih},
  timestamp = {2006.10.17}
}

@INPROCEEDINGS{weiss99segmentation,
  author = {Y. Weiss},
  title = {Segmentation using Eigenvectors: A Unifying View},
  booktitle = {{ICCV} (2)},
  year = {1999},
  pages = {975-982}
}

@ARTICLE{Husmeier2007NetworkPrior,
  author = {Werhli, A.V. and Husmeier, D.},
  title = {{Reconstructing gene regulatory networks with Bayesian networks by
	combining expression data with multiple sources of prior knowledge}},
  journal = {Statistical Applications in Genetics and Molecular Biology},
  year = {2007},
  volume = {6},
  number = {1},
  owner = {froehlih},
  timestamp = {2008.03.18}
}

@ARTICLE{Wessel98,
  author = {M. D. Wessel and P. C. Jurs and J. W. Tolan and S. M. Muskal},
  title = {{Prediction of Human Intestinal Absorption of Drug Compounds from
	Molecular Structure}},
  journal = {J. Chem. Inf. Comput. Sci.},
  year = {1998},
  volume = {38},
  pages = {726 - 735}
}

@PHDTHESIS{WestonPHD99,
  author = {J. Weston},
  title = {Extensions to the Support Vector Method},
  school = {Royal Holloway University of London},
  year = {1999}
}

@ARTICLE{WesEliSchTip02,
  author = {J. Weston and A. Elisseeff and B. Sch\"olkopf and M. Tipping},
  title = {{Use of the zero-norm with linear models and kernel methods}},
  journal = {J. Machine Learning Research Special Issue on Variable and Feature
	Selection},
  year = {2002},
  volume = {3},
  pages = {1439 - 1461}
}

@CONFERENCE{WesMuk01,
  author = {J. Weston and S. Mukherjee and O. Chapelle and M. Pontil and T. Poggio
	and V. Vapnik},
  title = {{Feature selection for SVMs}},
  booktitle = {Adv. Neural Inf. Proc. Syst. 13},
  year = {2001},
  editor = {S. Solla and T. Leen and K.-R. M\"uller},
  publisher = {MIT Press}
}

@CONFERENCE{WesWat99,
  author = {J. Weston and C. Watkins},
  title = {Multi-class support vector machines},
  booktitle = {Proc. Europ. Symp. Artificial Neural Networks},
  year = {1999},
  editor = {M. Verleysen},
  address = {Brussles}
}

@TECHREPORT{Whitley92,
  author = {D. Whitley},
  title = {{A Genetic Algorithm Tutorial}},
  institution = {Department of Computer Science, Colorado State University},
  year = {1993}
}

@TECHREPORT{WillamsGP97,
  author = {C. Willams},
  title = {Prediction With Gaussian Processes: From Linear Regression To Linear
	Prediction and Beyond},
  institution = {Aston University, UK},
  year = {1997},
  number = {NRG/97/012}
}

@ARTICLE{WolfeDual61,
  author = {P. Wolfe},
  title = {A duality theorem for nonlinear programming},
  journal = {Quarterly of Applied Mathematics},
  year = {1961},
  volume = {19},
  pages = {239 - 244}
}

@CONFERENCE{Wolpert97,
  author = {D. Wolpert and W. Macready},
  title = {{No Free Lunch Theorems for Optimization}},
  booktitle = {Proc. IEEE Transactions on Evolutionary Computation},
  year = {1997},
  volume = {1},
  number = {1},
  pages = {67 - 82}
}

@TECHREPORT{Wolpert95,
  author = {D. Wolpert and W. Macready},
  title = {{No Free Lunch Theorems for Search}},
  institution = {Santa Fee Institute},
  year = {1995},
  number = {SFI-TR-95-02-010}
}

@ARTICLE{Wu05SemiActive,
  author = {T. Wu and W. Pottenger},
  title = {A semi-supervised active learning algorithm for information extraction
	from textual data},
  journal = {Journal of the American Society for Information Science and Technology},
  year = {2005},
  volume = {56},
  pages = {258 - 271},
  number = {3}
}

@ARTICLE{xu01,
  author = {Xu, Y. and Olman, V. and Xu D.},
  title = {Clustering gene expression data using a graph theoretic approach:
	an application of minimum spanning trees},
  journal = {Bioinformatics},
  year = {2001},
  volume = {18},
  pages = {536-545},
  groupsearch = {0},
  keywords = {mathematical clustering}
}

@ARTICLE{XueQSARRFE04,
  author = {Y. Xue and Z. R. Li and C. W. Yap and L. Z. Sun and X. Chen and and
	Y. Z. Chen},
  title = {Effect of Molecular Descriptor Feature Selection in Support Vector
	Machine Classification of Pharmacokinetic and Toxicological Properties
	of Chemical Agents},
  journal = {J. Chem. Inf. Comp. Sci.},
  year = {2004},
  volume = {44},
  pages = {1630 - 1638},
  number = {5}
}

@INCOLLECTION{YamGeneCCA04,
  author = {Y. Yamanishi and J.-P. Vert and M. Kaneshisa},
  title = {Heterogenous Data Comparison and Gene Selection with Kernel Canonical
	Correlation Analysis},
  booktitle = {Kernel Methods in Computational Biology},
  publisher = {MIT Press},
  year = {2004},
  editor = {B. Sch\"olkopf and K. Tsuda and J.-P. Vert},
  pages = {209 - 230},
  address = {Cambridge, MA}
}

@ARTICLE{YamPNInfCCA04,
  author = {Y. Yamanishi and J.-P. Vert and M. Kaneshisa},
  title = {Protein network inference from multiple genomic data: a supervised
	approach},
  journal = {Bioinformatics},
  year = {2004},
  volume = {20},
  pages = {i363 - i370},
  number = {1}
}

@ARTICLE{YazHIA98,
  author = {M. Yazdanian and S. Glynn and J. Wright and A. Hawi},
  title = {Correlating Partitioning and Caco-2 Cell Permeability of Structurally
	Diverse Small Molecular Weight Compounds},
  journal = {Pharm. Res.},
  year = {1998},
  volume = {15},
  pages = {1490 - 1494}
}

@ARTICLE{YeeHIA97,
  author = {S. Yee},
  title = {In Vitro Permeability Across Caco-2 Cells (Colonic) Can Predict in
	Vivo (Small Intestinal) Absorption in Man - Fact or Myth},
  journal = {Pharm. Res.},
  year = {1997},
  volume = {14},
  pages = {763 - 766}
}

@ARTICLE{yeung01_2,
  author = {Yeung, K.Y and Haynor, D.R. and Ruzzo, W. L.},
  title = {Validating clustering for gene expression data},
  journal = {Bioinformatics},
  year = {2001},
  volume = {17},
  pages = {309-318}
}

@ARTICLE{yeung01_1,
  author = {Yeung, K.Y. and Ruzzo, W.L.},
  title = {Principal component analysis for clustering gene expression data.},
  journal = {Bioinformatics},
  year = {2001},
  volume = {17},
  pages = {763-774},
  groupsearch = {0},
  keywords = {other methods (pca)}
}

@ARTICLE{yoshida00,
  author = {F. Yoshida and J. Topliss},
  title = {{QSAR model for drug human oral bioavailability}},
  journal = {J. Med. Chem.},
  year = {2000},
  volume = {43},
  pages = {2575 - 2585}
}

@ARTICLE{Yoshikawa1996a,
  author = {T. Yoshikawa and B. R. DuPont and R. J. Leach and S. D. Detera-Wadleigh},
  title = {New variants of the human and rat nuclear hormone receptor, TR4:
	expression and chromosomal localization of the human gene.},
  journal = {Genomics},
  year = {1996},
  volume = {35},
  pages = {361--366},
  number = {2},
  month = {Jul},
  abstract = {TR4 is a new member of the nuclear hormone receptor family. This receptor
	is highly conserved in rat and human, but an in-frame insertion of
	19 amino acid residues in the amino-terminal (A/B) region was found
	in the human homolog, which we refer to as hTR4alpha1. By reverse
	transcription-PCR (RT-PCR) we have identified a human TR4 mRNA (hTR4alpha2)
	that is analogous in size and sequence to the reported rat TR4. RT-PCR
	analysis using total RNA derived from various rat tissues revealed
	a new rat TR4 transcript, referred to as rTR4alpha1, which is homologous
	to hTR4alpha1 since it contains the extra 19 amino acids in the A/B
	region. The two rat transcripts showed a differential tissue distribution.
	Analysis of the exon-intron organization of the hTR4 A/B region showed
	that the 19-amino-acid peptide insert in hTR4alpha1 was encoded by
	a separate exon, indicating that hTR4alpha1 and hTR4alpha2 transcripts
	were produced by the differential usage of the exon. RT-PCR analysis
	revealed that both hTR4alpha1 and hTR4alpha2 were detectable in brain,
	placenta, and ovary. In contrast, the human ovarian cancer cell line,
	PA1, failed to express hTR4alpha1. By fluorescence in situ hybridization,
	we have mapped the hTR4 gene to 3p25, a region deleted in some forms
	of cancer.},
  doi = {10.1006/geno.1996.0368},
  keywords = {Animals; Base Sequence; Brain; Chromosome Deletion; Chromosome Mapping;
	Chromosomes, Human, Pair 3; DNA Primers; Exons; Female; Humans; In
	Situ Hybridization, Fluorescence; Introns; Molecular Sequence Data;
	Neoplasms; Nerve Tissue Proteins; Organ Specificity; Ovary; Placenta;
	Polymerase Chain Reaction; Pregnancy; RNA, Messenger; Rats; Receptors,
	Cytoplasmic and Nuclear; Receptors, Steroid; Receptors, Thyroid Hormone;
	Transcription, Genetic; Variation (Genetics)},
  owner = {froehlih},
  pii = {S0888-7543(96)90368-7},
  pmid = {8661150},
  timestamp = {2008.04.02},
  url = {http://dx.doi.org/10.1006/geno.1996.0368}
}

@ARTICLE{Yu2000,
  author = {B. Yu and M. E. Lane and R. G. Pestell and C. Albanese and S. Wadler},
  title = {Downregulation of cyclin D1 alters cdk 4- and cdk 2-specific phosphorylation
	of retinoblastoma protein.},
  journal = {Mol Cell Biol Res Commun},
  year = {2000},
  volume = {3},
  pages = {352--359},
  number = {6},
  month = {Jun},
  abstract = {Progression of cells through the G1 phase of the cell cycle requires
	the assembly and activation of specific cyclin:cyclin-dependent kinase
	(cdk) complexes in a tightly regulated, sequential fashion. To more
	clearly define the temporal events leading to the G1/S transition,
	sequential changes in the expression of cyclin E and cdks 2, 4, and
	6, as well as the phosphorylation of the retinoblastoma protein (pRb),
	were assayed in RA28 cells, a variant of human colon cancer RKO cells
	which were modified by transfection of an ecdysone-inducible antisense
	(AS) CD1 expression system. Induction of cyclin D1 antisense mRNA
	by the ecdysteroid, ponasterone A, resulted in a 55\% decrease in
	cyclin D1 mRNA and a 58\% decrease in CD1 protein levels. There was
	a 2.4-fold decrease in the ratio of hyperphosphorylated pRb (ppRb)
	to hypophosphorylated pRb, as well as a 60-75\% decrease in cdk 2-
	and cdk 4-specific phosphorylated pRb proteins. Of interest, cyclin
	E-dependent phosphorylation (cdk2) decreased 2.5-fold at 3 h despite
	only a 30\% decrease in cyclin E protein level. Levels of cdk 2,
	cdk 4, and cdk 6 decreased 40-70\%, while levels of cyclin A and
	B were unaffected by induction of CD1 antisense. Induction of a CD1
	antisense gene in a human colon cancer cell line resulted in rapid,
	concomitant changes in CD1 mRNA and protein, cyclin E, cdk2, cdk4,
	and cdk6, as well as the ratio of ppRb to pRb. In this system, growth
	regulatory events are tightly regulated and the perturbed expression
	of a single protein, CD1, rapidly alters expression of multiple regulatory
	proteins involved in the G1/S transition phase of cell cycle progression.},
  doi = {10.1006/mcbr.2000.0238},
  institution = {Department of Medicine, Albert Einstein College of Medicine, Bronx,
	New York, 10461, USA.},
  keywords = {Blotting, Western; CDC2-CDC28 Kinases; Colonic Neoplasms, metabolism;
	Cyclin D1, genetics/metabolism; Cyclin E, metabolism; Cyclin-Dependent
	Kinase 2; Cyclin-Dependent Kinase 4; Cyclin-Dependent Kinase 6; Cyclin-Dependent
	Kinases, metabolism; Down-Regulation, drug effects; Ecdysterone,
	analogs /&/ derivatives/pharmacology; Gene Expression Regulation,
	Neoplastic, drug effects; Genes, Reporter, genetics; Humans; Phosphorylation,
	drug effects; Protein-Serine-Threonine Kinases, metabolism; Proto-Oncogene
	Proteins; RNA, Antisense, genetics; RNA, Messenger, genetics/metabolism;
	Receptors, Steroid, genetics; Retinoblastoma Protein, metabolism;
	Substrate Specificity; Transcriptional Activation, drug effects;
	Transfection; Tumor Cells, Cultured},
  owner = {holfro},
  pii = {S1522472400902381},
  pmid = {11032757},
  timestamp = {2009.02.20},
  url = {http://dx.doi.org/10.1006/mcbr.2000.0238}
}

@ARTICLE{YuFCBF04,
  author = {L. Yu and H. Liu},
  title = {Efficient Feature Selection via Analysis of Relvance and Redundancy},
  journal = {J. Machine Learning Research},
  year = {2004},
  volume = {5},
  pages = {1205 - 1224}
}

@ARTICLE{zahn71,
  author = {Zahn, C.T.},
  title = {Graph-theoretical methods for detecting and describing Gestalt clusters},
  journal = {IEEE Transactions on Computers},
  year = {1971},
  volume = {C-20},
  pages = {68-86},
  groupsearch = {0},
  keywords = {minimum spanning trees}
}

@ARTICLE{zeeberg03gominer,
  author = {B.R. Zeeberg and W. Feng and G. Wang and A.T. Fojo \textit{et al.}},
  title = {{GO}Miner: a resource for biological interpretation of genomic and
	proteomic data},
  journal = {Genome Biology},
  year = {2003},
  volume = {4},
  number = {R28}
}

@ARTICLE{Zeller2008NEMsBN,
  author = {C. Zeller and H. Fr\"ohlich and A. Tresch},
  title = {A Bayesian Network View on Nested Effects Models},
  journal = {EURASIP Journal on Bioinformatics and Systems Biology},
  year = {2009},
  volume = {195272},
  note = {In Press},
  owner = {froehlih},
  timestamp = {2008.11.06}
}

@CONFERENCE{DingKmeansSpectral,
  author = {H. Zha and C. Ding and M. Gu and X. He and H. Simon},
  title = {Spectral Relaxation for K-means Clustering},
  booktitle = {Proc. Neural Inf. Proc. Syst. 14},
  year = {2001},
  pages = {1057 - 1064}
}

@ARTICLE{Zhan2006a,
  author = {Lixing Zhan and Bin Xiang and Senthil K Muthuswamy},
  title = {Controlled activation of ErbB1/ErbB2 heterodimers promote invasion
	of three-dimensional organized epithelia in an ErbB1-dependent manner:
	implications for progression of ErbB2-overexpressing tumors.},
  journal = {Cancer Res},
  year = {2006},
  volume = {66},
  pages = {5201--5208},
  number = {10},
  month = {May},
  abstract = {Receptor tyrosine kinases of the ErbB family are implicated in a number
	of cancers, including that of the breast. ErbB receptors are activated
	by ligand-induced formation of homodimers and heterodimers. Receptor
	heterodimerization is thought to play a critical role in breast cancers
	overexpressing multiple members of the ErbB family. Although coexpression
	of ErbB receptors is associated with poor patient prognosis, the
	mechanisms by which receptor heterodimerization regulates tumor progression
	are not clear, due in part to a lack of methods that allow controlled
	activation of specific receptor heterodimers in mammary epithelial
	cells. Here, we report an approach to activate ErbB1-ErbB2 heterodimers
	in a nontumorigenic breast epithelial cell line, MCF-10A, without
	interference from endogenous ErbB receptors. Using such a method,
	we show that whereas both ErbB2 homodimers and ErbB1-ErbB2 heterodimers
	were equally potent in activating the Ras/mitogen-activated protein
	kinase pathway, the heterodimers were more potent in activating the
	phosphoinositide 3'-kinase (PI3K) and phospholipase Cgamma1 pathways
	than ErbB2 homodimers. We combined the dimerization system with a
	three-dimensional cell culture approach to show that whereas both
	ErbB2 homodimers and ErbB1-ErbB2 heterodimers induced disruption
	of three-dimensional acini-like structures, only heterodimers promoted
	invasion of cells through extracellular matrix. The ability of heterodimers
	to induce invasion required the ErbB1 kinase activity and required
	activation of PI3K, Ras/mitogen-activated protein kinase, and phospholipase
	Cgamma1 signaling pathways. Thus, we have identified cell invasion
	as a heterodimer-specific biological outcome and suggest that coexpression
	of ErbB1 may critically regulate invasive progression of ErbB2-positive
	breast cancers.},
  doi = {10.1158/0008-5472.CAN-05-4081},
  keywords = {Breast; Breast Neoplasms; Cell Transformation, Neoplastic; Dimerization;
	Disease Progression; Epithelial Cells; Humans; Neoplasm Invasiveness;
	Oncogene Proteins, Fusion; Receptor, Epidermal Growth Factor; Receptor,
	erbB-2; Signal Transduction},
  owner = {froehlih},
  pii = {66/10/5201},
  pmid = {16707444},
  timestamp = {2008.10.23},
  url = {http://dx.doi.org/10.1158/0008-5472.CAN-05-4081}
}

@ARTICLE{zhang99,
  author = {Zhang, M.},
  title = {Large-scale Gene Expression Data Analysis: A New Challenge to Computational
	Biologists.},
  journal = {Genome Research},
  year = {1999},
  volume = {9},
  pages = {681-688},
  groupsearch = {0},
  keywords = {reviews}
}

@CONFERENCE{ZhouSemiSup04,
  author = {D. Zhou and O. Bousquet and T. Lal and J. Weston and B. Sch\"olkopf},
  title = {Learning with Local and Global Consistency},
  booktitle = {Neural Inf. Proc. Syst.},
  year = {2004}
}

@CONFERENCE{Zhu03SemiActive,
  author = {Xiaojin Zhu and John Lafferty and Zoubin Ghahramani},
  title = {Semi-Supervised Learning Using Gaussian Fields and Harmonic Functions},
  booktitle = {ICML 2003 workshop on The Continuum from Labeled to Unlabeled Data
	in Machine Learning and Data Mining},
  year = {2003}
}

@INPROCEEDINGS{Zien00,
  author = {Zien, A. and K?ffner, R. and Zimmer, R. and Lengauer, T.},
  title = {Analysis of Gene Expression Data with Pathway Scores},
  booktitle = {Proceedings of the 8th International Conference on Intelligent Systems
	for Molecular Biology},
  year = {2000},
  editor = {R. Altman \textit{et al.}},
  pages = {407-417},
  address = {La Jolla, CA},
  groupsearch = {0},
  keywords = {pathway scoring methods}
}

@BOOK{BonchevGraphTheory90,
  title = {{{C}hemical {G}raph {T}heory: {I}ntroduction and {F}undamentals}},
  publisher = {Gordon and Breach Science Publishers},
  year = {1990},
  editor = {D. Bonchev and D. H. Rouvray},
  volume = {1},
  series = {Mathematical Chemistry Series},
  address = {London, UK},
  groupsearch = {0},
  isbn = {0--85626--454--7}
}

@BOOK{TodeschiniMolDescs00,
  title = {{H}andbook of {M}olecular {D}escriptors},
  publisher = {Wiley--VCH},
  year = {2000},
  editor = {R. Todeschini and V. Consonni},
  address = {Weinheim},
  groupsearch = {0},
  isbn = {3--52--29913--0}
}


@article{Niederberger2012,
    author = {Niederberger, , Theresa AND Etzold, , Stefanie AND Lidschreiber, , Michael AND Maier, , Kerstin C. AND Martin, , Dietmar E. AND Fröhlich, , Holger AND Cramer, , Patrick AND Tresch, , Achim},
    journal = {PLoS Comput Biol},
    publisher = {Public Library of Science},
    title = {MC EMiNEM Maps the Interaction Landscape of the Mediator},
    year = {2012},
    month = {06},
    volume = {8},
    url = {http://dx.doi.org/10.1371%2Fjournal.pcbi.1002568},
    pages = {e1002568},
    abstract = {<title>Author Summary</title><p>Phenotypic diversity and environmental adaptation in genetically identical cells is achieved by an exact tuning of their transcriptional program. It is a challenging task to unravel parts of the complex network of involved gene regulatory components and their interactions. Here, we shed light on the role of the Mediator complex in transcription regulation in yeast. The Mediator is highly conserved in all eukaryotes and acts as an interface between gene-specific transcription factors and the general mRNA transcription machinery. Even though most of the involved proteins and numerous structural features are already known, details on its functional contribution on basal as well as on activated transcription remain obscure. We use gene expression data, measured upon perturbations of various Mediator subunits, to relate the Mediator structure to the way it processes regulatory information. Moreover, we relate specific subunits to interacting transcription factors.</p>},
    number = {6},
    doi = {10.1371/journal.pcbi.1002568}
}        

