Bioconductor

Bioconductor provides tools for the analysis and comprehension of high-throughput genomic data. Bioconductor uses the R statistical programming language, and is open source and open development. It has two releases each year, 554 software packages, and an active user community. Bioconductor is also available as an Amazon Machine Image (AMI).


References in zbMATH (referenced in 127 articles , 2 standard articles )

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  1. Pruim, Randall: Foundations and applications of statistics. An introduction using R (2018)
  2. Bo Wang, Daniele Ramazzotti, Luca De Sano, Junjie Zhu, Emma Pierson, Serafim Batzoglou: SIMLR: a tool for large-scale single-cell analysis by multi-kernel learning (2017) arXiv
  3. Chang, Jinyuan; Zhou, Wen; Zhou, Wen-Xin; Wang, Lan: Comparing large covariance matrices under weak conditions on the dependence structure and its application to gene clustering (2017)
  4. Daniele Ramazzotti, Luca De Sano, Roberta Spinelli, Rocco Piazza, Carlo Gambacorti Passerini: OncoScore: an R package to measure the oncogenic potential of genes (2017) arXiv
  5. Gabriel Becker, Cory Barr, Robert Gentleman, Michael Lawrence: Enhancing Reproducibility and Collaboration via Management of R Package Cohorts (2017)
  6. Keith, Jonathan M. (ed.): Bioinformatics. Volume II: structure, function, and applications (2017)
  7. Shafaghati, Leila; Razaghi-Moghadam, Zahra; Mohammadnejad, Javad: A systems biology approach to understanding alcoholic liver disease molecular mechanism: the development of static and dynamic models (2017)
  8. Anders Bilgrau; Poul Eriksen; Jakob Rasmussen; Hans Johnsen; Karen Dybkaer; Martin Boegsted: GMCM: Unsupervised Clustering and Meta-Analysis Using Gaussian Mixture Copula Models (2016)
  9. Angelopoulos, Nicos; Abdallah, Samer; Giamas, Georgios: Advances in integrative statistics for logic programming (2016)
  10. Carugo, Oliviero (ed.); Eisenhaber, Frank (ed.): Data mining techniques for the life sciences (2016)
  11. Dasgupta, Nairanjana; Genz, Alan; Lazar, Nicole A.: A look at multiplicity through misclassification (2016)
  12. Li, Chung-I; Shyr, Yu: Sample size calculation based on generalized linear models for differential expression analysis in RNA-seq data (2016)
  13. Mathé, Ewy (ed.); Davis, Sean (ed.): Statistical genomics. Methods and protocols (2016)
  14. Phipson, Belinda; Lee, Stanley; Majewski, Ian J.; Alexander, Warren S.; Smyth, Gordon K.: Robust hyperparameter estimation protects against hypervariable genes and improves power to detect differential expression (2016)
  15. Ruddy, Sean; Johnson, Marla; Purdom, Elizabeth: Shrinkage of dispersion parameters in the binomial family, with application to differential exon skipping (2016)
  16. Vincenzo Lagani, Giorgos Athineou, Alessio Farcomeni, Michail Tsagris, Ioannis Tsamardinos: Feature Selection with the R Package MXM: Discovering Statistically-Equivalent Feature Subsets (2016) arXiv
  17. Aoshima, Makoto; Yata, Kazuyoshi: Asymptotic normality for inference on multisample, high-dimensional mean vectors under mild conditions (2015)
  18. Chekouo, Thierry; Murua, Alejandro; Raffelsberger, Wolfgang: The Gibbs-plaid biclustering model (2015)
  19. Lithio, Andrew; Nettleton, Dan: Hierarchical modeling and differential expression analysis for RNA-seq experiments with inbred and hybrid genotypes (2015)
  20. Luca De Sano, Giulio Caravagna, Daniele Ramazzotti, Alex Graudenzi, Giancarlo Mauri, Bud Mishra, Marco Antoniotti: TRONCO: an R package for the inference of cancer progression models from heterogeneous genomic data (2015) arXiv

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