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 225 articles , 2 standard articles )

Showing results 1 to 20 of 225.
Sorted by year (citations)

1 2 3 ... 10 11 12 next

  1. Bandara, Udika; Gill, Ryan; Mitra, Riten: On computing maximum likelihood estimates for the negative binomial distribution (2019)
  2. João Duarte; Vinícius Mayrink: slfm: An R Package to Evaluate Coherent Patterns in Microarray Data via Factor Analysis (2019) not zbMATH
  3. Jordi Martorell-Marugán, Víctor González-Rumayor, Pedro Carmona-Sáez: mCSEA: detecting subtle differentially methylated regions (2019) not zbMATH
  4. Kuan-Hao Chao, Yi-Wen Hsiao, Yi-Fang Lee, Chien-Yueh Lee, Liang-Chuan Lai, Mong-Hsun Tsai, Tzu-Pin Lu, Eric Y. Chuang: RNASeqR: an R package for automated two-group RNA-Seq analysis workflow (2019) arXiv
  5. Li, Ang; Barber, Rina Foygel: Multiple testing with the structure-adaptive Benjamini-Hochberg algorithm (2019)
  6. Michael Schubert: clustermq enables efficient parallelization of genomic analyses (2019) not zbMATH
  7. Ritz, Christian; Jensen, Signe Marie; Gerhard, Daniel; Streibig, Jens Carl: Dose-response analysis using R (2019)
  8. Dadaneh, Siamak Zamani; Qian, Xiaoning; Zhou, Mingyuan: BNP-seq: Bayesian nonparametric differential expression analysis of sequencing count data (2018)
  9. Esteves, Gustavo H.; Reis, Luiz F. L.: A statistical method for measuring activation of gene regulatory networks (2018)
  10. Franks, Alexander M.; Markowetz, Florian; Airoldi, Edoardo M.: Refining cellular pathway models using an ensemble of heterogeneous data sources (2018)
  11. Holmes, Susan: Statistical proof? The problem of irreproducibility (2018)
  12. Pruim, Randall: Foundations and applications of statistics. An introduction using R (2018)
  13. Rosenthal, Jeffrey S.; Yang, Jinyoung: Ergodicity of combocontinuous adaptive MCMC algorithms (2018)
  14. Smirnova, Ekaterina; Ivanescu, Andrada; Bai, Jiawei; Crainiceanu, Ciprian M.: A practical guide to big data (2018)
  15. Song, Wei; Liu, Huaping; Wang, Jiajia; Kong, Yan; Yin, Xia; Zang, Weidong: MATHT: a web server for comprehensive transcriptome data analysis (2018)
  16. von Stechow, Louise (ed.); Delgado, Alberto Santos (ed.): Computational cell biology. Methods and protocols (2018)
  17. Yang, Tae Young; Jeong, Seongmun: Controlling the false-discovery rate by procedures adapted to the length bias of RNA-seq (2018)
  18. Yu Sun; Siv G.E. Andersson: SSCU: an R/Bioconductor package for analyzing selective profile in synonymous codon usage (2018) arXiv
  19. Zhao, Lili; Wu, Weisheng; Feng, Dai; Jiang, Hui; Nguyen, Xuanlong: Bayesian analysis of RNA-Seq data using a family of negative binomial models (2018)
  20. 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

1 2 3 ... 10 11 12 next