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 276 articles , 2 standard articles )

Showing results 1 to 20 of 276.
Sorted by year (citations)

1 2 3 ... 12 13 14 next

  1. Granata, Ilaria; Guarracino, Mario R.; Kalyagin, Valery A.; Maddalena, Lucia; Manipur, Ichcha; Pardalos, Panos M.: Model simplification for supervised classification of metabolic networks (2020)
  2. Ritz, Christian; Jensen, Signe Marie; Gerhard, Daniel; Streibig, Jens Carl: Dose-response analysis using R (2020)
  3. Zhao, Sihai Dave; Nguyen, Yet Tien: Nonparametric false discovery rate control for identifying simultaneous signals (2020)
  4. Bandara, Udika; Gill, Ryan; Mitra, Riten: On computing maximum likelihood estimates for the negative binomial distribution (2019)
  5. Bhattacharjee, Atanu; Vishwakarma, Gajendra K.: Time-course data prediction for repeatedly measured gene expression (2019)
  6. Chakraborty, Sounak; Lozano, Aurelie C.: A graph Laplacian prior for Bayesian variable selection and grouping (2019)
  7. de Campos, Luis M.; Cano, Andrés; Castellano, Javier G.; Moral, Serafín: Combining gene expression data and prior knowledge for inferring gene regulatory networks via Bayesian networks using structural restrictions (2019)
  8. João Duarte; Vinícius Mayrink: slfm: An R Package to Evaluate Coherent Patterns in Microarray Data via Factor Analysis (2019) not zbMATH
  9. Jordi Martorell-Marugán, Víctor González-Rumayor, Pedro Carmona-Sáez: mCSEA: detecting subtle differentially methylated regions (2019) not zbMATH
  10. Kiihl, Samara F.; Martinez-Garrido, Maria Jose; Domingo-Relloso, Arce; Bermudez, Jose; Tellez-Plaza, Maria: \textttMLML2R: an R package for maximum likelihood estimation of DNA methylation and hydroxymethylation proportions (2019)
  11. 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
  12. Li, Ang; Barber, Rina Foygel: Multiple testing with the structure-adaptive Benjamini-Hochberg algorithm (2019)
  13. Luo, Xiangyu; Wei, Yingying: Batch effects correction with unknown subtypes (2019)
  14. MacDonald, Peter W.; Liang, Kun; Janssen, Arnold: Dynamic adaptive procedures that control the false discovery rate (2019)
  15. Michael Schubert: clustermq enables efficient parallelization of genomic analyses (2019) not zbMATH
  16. Schlosser, Pascal: Netboost: statistical modeling strategies for high-dimensional data (2019)
  17. 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)
  18. Yuan, Chaofeng; Zhu, Wensheng; He, Xuming; Guo, Jianhua: A mixture factor model with applications to microarray data (2019)
  19. Carmichael, Iain; Marron, J. S.: Data science vs. statistics: two cultures? (2018)
  20. Dadaneh, Siamak Zamani; Qian, Xiaoning; Zhou, Mingyuan: BNP-seq: Bayesian nonparametric differential expression analysis of sequencing count data (2018)

1 2 3 ... 12 13 14 next