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 281 articles , 2 standard articles )

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  1. Schlosser, Pascal: Netboost: statistical modeling strategies for high-dimensional data (2019)
  2. Sen, Liang; Sen, Yang; Dayang, Liang; Jiechao, Ma; Yuan, Tian; Jing, Zhao; Xu, Zhang; Ying, Xu; Yan, Wang: A novel matched-pairs feature selection method considering with tumor purity for differential gene expression analyses (2019)
  3. Yuan, Chaofeng; Zhu, Wensheng; He, Xuming; Guo, Jianhua: A mixture factor model with applications to microarray data (2019)
  4. Carmichael, Iain; Marron, J. S.: Data science vs. statistics: two cultures? (2018)
  5. Dadaneh, Siamak Zamani; Qian, Xiaoning; Zhou, Mingyuan: BNP-seq: Bayesian nonparametric differential expression analysis of sequencing count data (2018)
  6. Esteves, Gustavo H.; Reis, Luiz F. L.: A statistical method for measuring activation of gene regulatory networks (2018)
  7. Franks, Alexander M.; Markowetz, Florian; Airoldi, Edoardo M.: Refining cellular pathway models using an ensemble of heterogeneous data sources (2018)
  8. Holmes, Susan: Statistical proof? The problem of irreproducibility (2018)
  9. Page, Christian M.; Vos, Linda; Rounge, Trine B.; Harbo, Hanne F.; Andreassen, Bettina K.: Assessing genome-wide significance for the detection of differentially methylated regions (2018)
  10. Pruim, Randall: Foundations and applications of statistics. An introduction using R (2018)
  11. Rosenthal, Jeffrey S.; Yang, Jinyoung: Ergodicity of combocontinuous adaptive MCMC algorithms (2018)
  12. Smirnova, Ekaterina; Ivanescu, Andrada; Bai, Jiawei; Crainiceanu, Ciprian M.: A practical guide to big data (2018)
  13. Song, Wei; Liu, Huaping; Wang, Jiajia; Kong, Yan; Yin, Xia; Zang, Weidong: MATHT: a web server for comprehensive transcriptome data analysis (2018)
  14. von Stechow, Louise (ed.); Delgado, Alberto Santos (ed.): Computational cell biology. Methods and protocols (2018)
  15. Yang, Tae Young; Jeong, Seongmun: Controlling the false-discovery rate by procedures adapted to the length bias of RNA-seq (2018)
  16. Yu Sun; Siv G.E. Andersson: SSCU: an R/Bioconductor package for analyzing selective profile in synonymous codon usage (2018) arXiv
  17. Zhao, Lili; Wu, Weisheng; Feng, Dai; Jiang, Hui; Nguyen, Xuanlong: Bayesian analysis of RNA-Seq data using a family of negative binomial models (2018)
  18. 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
  19. 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)
  20. 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

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