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 308 articles , 2 standard articles )

Showing results 41 to 60 of 308.
Sorted by year (citations)

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  1. Esteves, Gustavo H.; Reis, Luiz F. L.: A statistical method for measuring activation of gene regulatory networks (2018)
  2. Franks, Alexander M.; Markowetz, Florian; Airoldi, Edoardo M.: Refining cellular pathway models using an ensemble of heterogeneous data sources (2018)
  3. Holmes, Susan: Statistical proof? The problem of irreproducibility (2018)
  4. Page, Christian M.; Vos, Linda; Rounge, Trine B.; Harbo, Hanne F.; Andreassen, Bettina K.: Assessing genome-wide significance for the detection of differentially methylated regions (2018)
  5. Pruim, Randall: Foundations and applications of statistics. An introduction using R (2018)
  6. Rosenthal, Jeffrey S.; Yang, Jinyoung: Ergodicity of combocontinuous adaptive MCMC algorithms (2018)
  7. Smirnova, Ekaterina; Ivanescu, Andrada; Bai, Jiawei; Crainiceanu, Ciprian M.: A practical guide to big data (2018)
  8. Song, Wei; Liu, Huaping; Wang, Jiajia; Kong, Yan; Yin, Xia; Zang, Weidong: MATHT: a web server for comprehensive transcriptome data analysis (2018)
  9. von Stechow, Louise (ed.); Delgado, Alberto Santos (ed.): Computational cell biology. Methods and protocols (2018)
  10. Wélliton de Souza, Benilton de Sá Carvalho, Iscia Lopes-Cendes: Rqc: A Bioconductor Package for Quality Control of High-Throughput Sequencing Data (2018) not zbMATH
  11. Yang, Tae Young; Jeong, Seongmun: Controlling the false-discovery rate by procedures adapted to the length bias of RNA-seq (2018)
  12. Yu Sun; Siv G.E. Andersson: SSCU: an R/Bioconductor package for analyzing selective profile in synonymous codon usage (2018) arXiv
  13. Zhao, Lili; Wu, Weisheng; Feng, Dai; Jiang, Hui; Nguyen, Xuanlong: Bayesian analysis of RNA-Seq data using a family of negative binomial models (2018)
  14. 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
  15. Chang, Jinyuan; Zhou, Wen; Zhou, Wen-Xin; Wang, Lan: Comparing large covariance matrices under weak conditions on the dependence structure and its application to gene clustering (2017)
  16. Chen, Ding-Geng (Din); Peace, Karl E.; Zhang, Pinggao: Clinical trial data analysis using R and SAS (2017)
  17. Daniele Ramazzotti, Luca De Sano, Roberta Spinelli, Rocco Piazza, Carlo Gambacorti Passerini: OncoScore: an R package to measure the oncogenic potential of genes (2017) arXiv
  18. Friedrichs, Stefanie; Manitz, Juliane; Burger, Patricia; Amos, Christopher I.; Risch, Angela; Chang-Claude, Jenny; Wichmann, Heinz-Erich; Kneib, Thomas; Bickeböller, Heike; Hofner, Benjamin: Pathway-based kernel boosting for the analysis of genome-wide association studies (2017)
  19. Gabriel Becker, Cory Barr, Robert Gentleman, Michael Lawrence: Enhancing Reproducibility and Collaboration via Management of R Package Cohorts (2017) not zbMATH
  20. Islam, Shofiqul; Anand, Sonia; Hamid, Jemila; Thabane, Lehana; Beyene, Joseph: Comparing the performance of linear and nonlinear principal components in the context of high-dimensional genomic data integration (2017)

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