@article{milligan1996measuring,
  title={Measuring the influence of individual data points in a cluster analysis},
  author={Milligan, Glenn W and Cheng, Richard},
  journal={Journal of classification},
  volume={13},
  number={2},
  pages={315--335},
  year={1996},
  publisher={Springer}
}


@Article{multiAssayExperiment,
    title = {Software For The Integration Of Multi-Omics Experiments In Bioconductor},
    author = {Marcel Ramos and Lucas Schiffer and Angela Re and Rimsha Azhar and Azfar Basunia and Carmen Rodriguez Cabrera and Tiffany Chan and Philip Chapman and Sean Davis and David Gomez-Cabrero and Aedin C. Culhane and Benjamin Haibe-Kains and Kasper Hansen and Hanish Kodali and Marie Stephie Louis and Arvind Singh Mer and Markus Reister and Martin Morgan and Vincent Carey and Levi Waldron},
    journal = {Cancer Research},
    year = {2017},
    volume = {77(21); e39-42},
  }

@Manual{summarizedExperiment,
  title = {SummarizedExperiment: SummarizedExperiment container},
  author = {Martin Morgan and Valerie Obenchain and Jim Hester and Hervé Pagès},
  year = {2018},
  note = {R package version 1.12.0},
}

@article{jaccard1901distribution,
  title={Distribution de la flore alpine dans le bassin des Dranses et dans quelques regions voisines},
  author={Jaccard, Paul},
  journal={Bull Soc Vaudoise Sci Nat},
  volume={37},
  pages={241--272},
  year={1901}
}

@book{kaufman2009finding,
  title={Finding groups in data: an introduction to cluster analysis},
  author={Kaufman, Leonard and Rousseeuw, Peter J},
  volume={344},
  year={2009},
  publisher={John Wiley & Sons}
}

% cluster
@Manual{cluster2018,
    title = {R package "cluster": "Finding Groups in Data": Cluster Analysis Extended Rousseeuw et al"},
    author = {Maechler, Martin and Rousseeuw, Peter and Struyf, Anja and Hubert, Mia and Hornik, Kurt and Studer, Matthias and Roudier, Pierre and Gonzalez, Juan and Kozlowski, Kamil},
    year = {2018},
    note = {(2.0.7-1)},
    url = {https://cran.r-project.org/package=cluster},
}


% Corrplot
@Manual{corrplot2017,
    title = {R package "corrplot": Visualization of a Correlation Matrix},
    author = {Taiyun Wei and Viliam Simko},
    year = {2017},
    note = {(Version 0.84)},
    url = {https://github.com/taiyun/corrplot},
}

%rcore
@Manual{rcore,
    title = {R: A Language and Environment for Statistical Computing},
    author = {{R Core Team}},
    organization = {R Foundation for Statistical Computing},
    address = {Vienna, Austria},
    year = {2018},
    url = {https://www.R-project.org/},
}

% bioinformatics-briefings
@article{ontoeval,
    author = {Franco, Manuel and Vivo, Juana María and Quesada-Martínez, Manuel and Duque-Ramos, Astrid and Fernández-Breis, Jesualdo Tomás},
    title = "{Evaluation of ontology structural metrics based on public repository data}",
    year = {2019},
    month = {02},
    journal={Bioinformatics},
    abstract = "{The development and application of biological ontologies have increased significantly in recent years. These ontologies can be retrieved from different repositories, which do not provide much information about quality aspects of the ontologies. In the past years, some ontology structural metrics have been proposed, but their validity as measurement instrument has not been sufficiently studied to date. In this work, we evaluate a set of reproducible and objective ontology structural metrics. Given the lack of standard methods for this purpose, we have applied an evaluation method based on the stability and goodness of the classifications of ontologies produced by each metric on an ontology corpus. The evaluation has been done using ontology repositories as corpora. More concretely, we have used 119 ontologies from the OBO Foundry repository and 78 ontologies from AgroPortal. First, we study the correlations between the metrics. Second, we study whether the clusters for a given metric are stable and have a good structure. The results show that the existing correlations are not biasing the evaluation, there are no metrics generating unstable clusterings and all the metrics evaluated provide at least reasonable clustering structure. Furthermore, our work permits to review and suggest the most reliable ontology structural metrics in terms of stability and goodness of their classifications.Availability: http://sele.inf.um.es/ontology-metrics}",
    doi = {10.1093/bib/bbz009},
    url = {https://dx.doi.org/10.1093/bib/bbz009}
}


%rnametrics
@article{imbeaud2005towards,
  title={Towards standardization of RNA quality assessment using user-independent classifiers of microcapillary electrophoresis traces},
  author={Imbeaud, Sandrine and Graudens, Esther and Boulanger, Virginie and Barlet, Xavier and Zaborski, Patrick and Eveno, Eric and Mueller, Odilo and Schroeder, Andreas and Auffray, Charles},
  journal={Nucleic acids research},
  volume={33},
  number={6},
  pages={e56--e56},
  year={2005},
  publisher={Oxford University Press}
}

%pathways metrics
@article{davis2018metrics,
  title={Metrics for regulated biochemical pathway systems},
  author={Davis, Jacob D and Voit, Eberhard O},
  journal={Bioinformatics},
  doi={10.1093/bioinformatics/bty942},
  year={2018}
}
