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 282 articles , 2 standard articles )

Showing results 41 to 60 of 282.
Sorted by year (citations)

previous 1 2 3 4 5 ... 13 14 15 next

  1. 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)
  2. Gabriel Becker, Cory Barr, Robert Gentleman, Michael Lawrence: Enhancing Reproducibility and Collaboration via Management of R Package Cohorts (2017) not zbMATH
  3. 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)
  4. Keith, Jonathan M. (ed.): Bioinformatics. Volume II: structure, function, and applications (2017)
  5. Kotoka, Ekua; Orr, Megan: Modifying SAMseq to account for asymmetry in the distribution of effect sizes when identifying differentially expressed genes (2017)
  6. Lun, Aaron T. L.; Smyth, Gordon K.: No counts, no variance: allowing for loss of degrees of freedom when assessing biological variability from RNA-seq data (2017)
  7. Nie, Zixin; Liang, Kun: Adaptive filtering increases power to detect differentially expressed genes (2017)
  8. Papastamoulis, Panagiotis; Rattray, Magnus: Bayesian estimation of differential transcript usage from RNA-seq data (2017)
  9. Shafaghati, Leila; Razaghi-Moghadam, Zahra; Mohammadnejad, Javad: A systems biology approach to understanding alcoholic liver disease molecular mechanism: the development of static and dynamic models (2017)
  10. Tsai, Arthur C.; Liou, Michelle; Simak, Maria; Cheng, Philip E.: On hyperbolic transformations to normality (2017)
  11. Anders Bilgrau; Poul Eriksen; Jakob Rasmussen; Hans Johnsen; Karen Dybkaer; Martin Boegsted: GMCM: Unsupervised Clustering and Meta-Analysis Using Gaussian Mixture Copula Models (2016) not zbMATH
  12. Angelopoulos, Nicos; Abdallah, Samer; Giamas, Georgios: Advances in integrative statistics for logic programming (2016)
  13. Berk Ekmekci, Charles E. McAnany, Cameron Mura: An Introduction to Programming for Bioscientists: A Python-based Primer (2016) arXiv
  14. Bonafede, Elisabetta; Picard, Franck; Robin, Stéphane; Viroli, Cinzia: Modeling overdispersion heterogeneity in differential expression analysis using mixtures (2016)
  15. Carugo, Oliviero (ed.); Eisenhaber, Frank (ed.): Data mining techniques for the life sciences (2016)
  16. Dasgupta, Nairanjana; Genz, Alan; Lazar, Nicole A.: A look at multiplicity through misclassification (2016)
  17. Kruppa, Jochen; Kramer, Frank; Beißbarth, Tim; Jung, Klaus: A simulation framework for correlated count data of features subsets in high-throughput sequencing or proteomics experiments (2016)
  18. Li, Chung-I; Shyr, Yu: Sample size calculation based on generalized linear models for differential expression analysis in RNA-seq data (2016)
  19. Lin, Zhixiang; Li, Mingfeng; Sestan, Nenad; Zhao, Hongyu: A Markov random field-based approach for joint estimation of differentially expressed genes in mouse transcriptome data (2016)
  20. Mathé, Ewy (ed.); Davis, Sean (ed.): Statistical genomics. Methods and protocols (2016)

previous 1 2 3 4 5 ... 13 14 15 next