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 284 articles , 2 standard articles )

Showing results 1 to 20 of 284.
Sorted by year (citations)

1 2 3 ... 13 14 15 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. Martin, Bryan D.; Witten, Daniela; Willis, Amy D.: Modeling microbial abundances and dysbiosis with beta-binomial regression (2020)
  3. Philippe Boileau, Nima Hejazi, Sandrine Dudoit: scPCA: A toolbox for sparse contrastive principal component analysis in R (2020) not zbMATH
  4. Ren, Boyu; Bacallado, Sergio; Favaro, Stefano; Vatanen, Tommi; Huttenhower, Curtis; Trippa, Lorenzo: Bayesian mixed effects models for zero-inflated compositions in microbiome data analysis (2020)
  5. Ritz, Christian; Jensen, Signe Marie; Gerhard, Daniel; Streibig, Jens Carl: Dose-response analysis using R (2020)
  6. Wu, Tung-Lung; Li, Ping: Projected tests for high-dimensional covariance matrices (2020)
  7. Zhao, Sihai Dave; Nguyen, Yet Tien: Nonparametric false discovery rate control for identifying simultaneous signals (2020)
  8. Bandara, Udika; Gill, Ryan; Mitra, Riten: On computing maximum likelihood estimates for the negative binomial distribution (2019)
  9. Benjamini, Yuval; Taylor, Jonathan; Irizarry, Rafael A.: Selection-corrected statistical inference for region detection with high-throughput assays (2019)
  10. Bhattacharjee, Atanu; Vishwakarma, Gajendra K.: Time-course data prediction for repeatedly measured gene expression (2019)
  11. Chakraborty, Sounak; Lozano, Aurelie C.: A graph Laplacian prior for Bayesian variable selection and grouping (2019)
  12. 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)
  13. João Duarte; Vinícius Mayrink: slfm: An R Package to Evaluate Coherent Patterns in Microarray Data via Factor Analysis (2019) not zbMATH
  14. Jordi Martorell-Marugán, Víctor González-Rumayor, Pedro Carmona-Sáez: mCSEA: detecting subtle differentially methylated regions (2019) not zbMATH
  15. 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)
  16. 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
  17. Li, Ang; Barber, Rina Foygel: Multiple testing with the structure-adaptive Benjamini-Hochberg algorithm (2019)
  18. Luo, Xiangyu; Wei, Yingying: Batch effects correction with unknown subtypes (2019)
  19. MacDonald, Peter W.; Liang, Kun; Janssen, Arnold: Dynamic adaptive procedures that control the false discovery rate (2019)
  20. Madsen, Tobias; Świtnicki, Michał; Juul, Malene; Skou Pedersen, Jakob: \textttEBADIMEX: an empirical Bayes approach to detect joint differential expression and methylation and to classify samples (2019)

1 2 3 ... 13 14 15 next