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 94 articles , 2 standard articles )

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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. 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
  3. Angelopoulos, Nicos; Abdallah, Samer; Giamas, Georgios: Advances in integrative statistics for logic programming (2016)
  4. Carugo, Oliviero (ed.); Eisenhaber, Frank (ed.): Data mining techniques for the life sciences (2016)
  5. Dasgupta, Nairanjana; Genz, Alan; Lazar, Nicole A.: A look at multiplicity through misclassification (2016)
  6. Li, Chung-I; Shyr, Yu: Sample size calculation based on generalized linear models for differential expression analysis in RNA-seq data (2016)
  7. Mathé, Ewy (ed.); Davis, Sean (ed.): Statistical genomics. Methods and protocols (2016)
  8. 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)
  9. Aoshima, Makoto; Yata, Kazuyoshi: Asymptotic normality for inference on multisample, high-dimensional mean vectors under mild conditions (2015)
  10. Lithio, Andrew; Nettleton, Dan: Hierarchical modeling and differential expression analysis for RNA-seq experiments with inbred and hybrid genotypes (2015)
  11. 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
  12. Mayrink, Vinicius D.; Lucas, Joseph E.: Bayesian factor models for the detection of coherent patterns in gene expression data (2015)
  13. 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)
  14. Niemi, Jarad; Mittman, Eric; Landau, Will; Nettleton, Dan: Empirical Bayes analysis of RNA-seq data for detection of gene expression heterosis (2015)
  15. Picardi, Ernesto (ed.): RNA bioinformatics (2015)
  16. Carey, Vincent (ed.); Cook, Dianne (ed.): Four papers on contemporary software design strategies for statistical methodologists (2014)
  17. Caserta, Marco; Voß, Stefan: A hybrid algorithm for the DNA sequencing problem (2014)
  18. Chambers, John M.: Object-oriented programming, functional programming and R (2014)
  19. Datta, Somnath (ed.); Nettleton, Dan (ed.): Statistical analysis of next generation sequencing data (2014)
  20. Gu, Jian-lei; Lu, Yao; Liu, Cong; Lu, Hui: Multiclass classification of sarcomas using pathway based feature selection method (2014)

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