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 112 articles , 2 standard articles )

Showing results 1 to 20 of 112.
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  1. 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
  2. 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)
  3. 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
  4. Angelopoulos, Nicos; Abdallah, Samer; Giamas, Georgios: Advances in integrative statistics for logic programming (2016)
  5. Carugo, Oliviero (ed.); Eisenhaber, Frank (ed.): Data mining techniques for the life sciences (2016)
  6. Dasgupta, Nairanjana; Genz, Alan; Lazar, Nicole A.: A look at multiplicity through misclassification (2016)
  7. Li, Chung-I; Shyr, Yu: Sample size calculation based on generalized linear models for differential expression analysis in RNA-seq data (2016)
  8. Mathé, Ewy (ed.); Davis, Sean (ed.): Statistical genomics. Methods and protocols (2016)
  9. 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)
  10. Ruddy, Sean; Johnson, Marla; Purdom, Elizabeth: Shrinkage of dispersion parameters in the binomial family, with application to differential exon skipping (2016)
  11. Vincenzo Lagani, Giorgos Athineou, Alessio Farcomeni, Michail Tsagris, Ioannis Tsamardinos: Feature Selection with the R Package MXM: Discovering Statistically-Equivalent Feature Subsets (2016) arXiv
  12. Aoshima, Makoto; Yata, Kazuyoshi: Asymptotic normality for inference on multisample, high-dimensional mean vectors under mild conditions (2015)
  13. Chekouo, Thierry; Murua, Alejandro; Raffelsberger, Wolfgang: The Gibbs-plaid biclustering model (2015)
  14. Lithio, Andrew; Nettleton, Dan: Hierarchical modeling and differential expression analysis for RNA-seq experiments with inbred and hybrid genotypes (2015)
  15. 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
  16. Mayrink, Vinicius D.; Lucas, Joseph E.: Bayesian factor models for the detection of coherent patterns in gene expression data (2015)
  17. Nguyen, Yet; Nettleton, Dan; Liu, Haibo; Tuggle, Christopher K.: Detecting differentially expressed genes with RNA-seq data using backward selection to account for the effects of relevant covariates (2015)
  18. Niemi, Jarad; Mittman, Eric; Landau, Will; Nettleton, Dan: Empirical Bayes analysis of RNA-seq data for detection of gene expression heterosis (2015)
  19. Picardi, Ernesto (ed.): RNA bioinformatics (2015)
  20. Wang, Zhishi; He, Qiuling; Larget, Bret; Newton, Michael A.: A multi-functional analyzer uses parameter constraints to improve the efficiency of model-based gene-set analysis (2015)

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