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 145 articles , 2 standard articles )

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  1. Esteves, Gustavo H.; Reis, Luiz F. L.: A statistical method for measuring activation of gene regulatory networks (2018)
  2. Pruim, Randall: Foundations and applications of statistics. An introduction using R (2018)
  3. Yu Sun; Siv G.E. Andersson: SSCU: an R/Bioconductor package for analyzing selective profile in synonymous codon usage (2018) arXiv
  4. 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
  5. 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)
  6. 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
  7. Gabriel Becker, Cory Barr, Robert Gentleman, Michael Lawrence: Enhancing Reproducibility and Collaboration via Management of R Package Cohorts (2017)
  8. Keith, Jonathan M. (ed.): Bioinformatics. Volume II: structure, function, and applications (2017)
  9. 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)
  10. Tsai, Arthur C.; Liou, Michelle; Simak, Maria; Cheng, Philip E.: On hyperbolic transformations to normality (2017)
  11. Anders Bilgrau; Poul Eriksen; Jakob Rasmussen; Hans Johnsen; Karen Dybkaer; Martin Boegsted: GMCM: Unsupervised Clustering and Meta-Analysis Using Gaussian Mixture Copula Models (2016)
  12. Angelopoulos, Nicos; Abdallah, Samer; Giamas, Georgios: Advances in integrative statistics for logic programming (2016)
  13. Carugo, Oliviero (ed.); Eisenhaber, Frank (ed.): Data mining techniques for the life sciences (2016)
  14. Dasgupta, Nairanjana; Genz, Alan; Lazar, Nicole A.: A look at multiplicity through misclassification (2016)
  15. Li, Chung-I; Shyr, Yu: Sample size calculation based on generalized linear models for differential expression analysis in RNA-seq data (2016)
  16. Mathé, Ewy (ed.); Davis, Sean (ed.): Statistical genomics. Methods and protocols (2016)
  17. 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)
  18. Ruddy, Sean; Johnson, Marla; Purdom, Elizabeth: Shrinkage of dispersion parameters in the binomial family, with application to differential exon skipping (2016)
  19. van Wieringen, Wessel N.; Peeters, Carel F. W.: Ridge estimation of inverse covariance matrices from high-dimensional data (2016)
  20. Vincenzo Lagani, Giorgos Athineou, Alessio Farcomeni, Michail Tsagris, Ioannis Tsamardinos: Feature Selection with the R Package MXM: Discovering Statistically-Equivalent Feature Subsets (2016) arXiv

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