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 305 articles , 2 standard articles )

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  1. Doğru, Fatma Zehra; Arslan, Olcay: Finite mixtures of skew Laplace normal distributions with random skewness (2021)
  2. Jacques Serizay: Generating fragment density plots in R/Bioconductor with VplotR (2021) not zbMATH
  3. Granata, Ilaria; Guarracino, Mario R.; Kalyagin, Valery A.; Maddalena, Lucia; Manipur, Ichcha; Pardalos, Panos M.: Model simplification for supervised classification of metabolic networks (2020)
  4. Martin, Bryan D.; Witten, Daniela; Willis, Amy D.: Modeling microbial abundances and dysbiosis with beta-binomial regression (2020)
  5. 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
  6. Mayrink, Vinícius Diniz; Gonçalves, Flávio B.: Identifying atypically expressed chromosome regions using RNA-Seq data (2020)
  7. Philippe Boileau, Nima Hejazi, Sandrine Dudoit: scPCA: A toolbox for sparse contrastive principal component analysis in R (2020) not zbMATH
  8. Pusuluri, Krishna; Basodi, Sunitha; Shilnikov, Andrey: Computational exposition of multistable rhythms in 4-cell neural circuits (2020)
  9. 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)
  10. Ritz, Christian; Jensen, Signe Marie; Gerhard, Daniel; Streibig, Jens Carl: Dose-response analysis using R (2020)
  11. Shuler, Kurtis; Sison-Mangus, Marilou; Lee, Juhee: Bayesian sparse multivariate regression with asymmetric nonlocal priors for microbiome data analysis (2020)
  12. Wu, Tung-Lung; Li, Ping: Projected tests for high-dimensional covariance matrices (2020)
  13. Zhao, Sihai Dave; Nguyen, Yet Tien: Nonparametric false discovery rate control for identifying simultaneous signals (2020)
  14. Zhuo, Bin; Jiang, Duo; Di, Yanming: Test-statistic correlation and data-row correlation (2020)
  15. Bandara, Udika; Gill, Ryan; Mitra, Riten: On computing maximum likelihood estimates for the negative binomial distribution (2019)
  16. Benjamini, Yuval; Taylor, Jonathan; Irizarry, Rafael A.: Selection-corrected statistical inference for region detection with high-throughput assays (2019)
  17. Bhattacharjee, Atanu; Vishwakarma, Gajendra K.: Time-course data prediction for repeatedly measured gene expression (2019)
  18. Bucur, Ioan Gabriel; Claassen, Tom; Heskes, Tom: Large-scale local causal inference of gene regulatory relationships (2019)
  19. Chakraborty, Sounak; Lozano, Aurelie C.: A graph Laplacian prior for Bayesian variable selection and grouping (2019)
  20. 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)

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