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 298 articles , 2 standard articles )

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  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. Mathieu Emily, Nicolas Sounac, Florian Kroell, Magalie Houée-Bigot: Gene-Based Methods to Detect Gene-Gene Interaction in R: The GeneGeneInteR Package (2020) not zbMATH
  4. Philippe Boileau, Nima Hejazi, Sandrine Dudoit: scPCA: A toolbox for sparse contrastive principal component analysis in R (2020) not zbMATH
  5. Pusuluri, Krishna; Basodi, Sunitha; Shilnikov, Andrey: Computational exposition of multistable rhythms in 4-cell neural circuits (2020)
  6. 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)
  7. Ritz, Christian; Jensen, Signe Marie; Gerhard, Daniel; Streibig, Jens Carl: Dose-response analysis using R (2020)
  8. Wu, Tung-Lung; Li, Ping: Projected tests for high-dimensional covariance matrices (2020)
  9. Zhao, Sihai Dave; Nguyen, Yet Tien: Nonparametric false discovery rate control for identifying simultaneous signals (2020)
  10. Zhuo, Bin; Jiang, Duo; Di, Yanming: Test-statistic correlation and data-row correlation (2020)
  11. Bandara, Udika; Gill, Ryan; Mitra, Riten: On computing maximum likelihood estimates for the negative binomial distribution (2019)
  12. Benjamini, Yuval; Taylor, Jonathan; Irizarry, Rafael A.: Selection-corrected statistical inference for region detection with high-throughput assays (2019)
  13. Bhattacharjee, Atanu; Vishwakarma, Gajendra K.: Time-course data prediction for repeatedly measured gene expression (2019)
  14. Bucur, Ioan Gabriel; Claassen, Tom; Heskes, Tom: Large-scale local causal inference of gene regulatory relationships (2019)
  15. Chakraborty, Sounak; Lozano, Aurelie C.: A graph Laplacian prior for Bayesian variable selection and grouping (2019)
  16. 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)
  17. João Duarte; Vinícius Mayrink: slfm: An R Package to Evaluate Coherent Patterns in Microarray Data via Factor Analysis (2019) not zbMATH
  18. Jordi Martorell-Marugán, Víctor González-Rumayor, Pedro Carmona-Sáez: mCSEA: detecting subtle differentially methylated regions (2019) not zbMATH
  19. 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)
  20. 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

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