Cross-platform normalization of microarray and RNA-seq data for machine learning applications

Article English OPEN
Thompson, Jeffrey A.; Tan, Jie; Greene, Casey S.;
(2016)
  • Publisher: PeerJ Inc.
  • Journal: PeerJ, volume 4 (issn: 2167-8359, eissn: 2167-8359)
  • Publisher copyright policies & self-archiving
  • Related identifiers: doi: 10.7717/peerj.1621, pmc: PMC4736986
  • Subject: Computational Biology | RNA-sequencing | Quantile normalization | Medicine | Training | Machine learning | Distribution | Microarray | R | Normalization | Bioinformatics | Genomics | Nonparanormal transformation | Cross-platform normalization | Gene expression
    acm: ComputingMethodologies_PATTERNRECOGNITION

Large, publicly available gene expression datasets are often analyzed with the aid of machine learning algorithms. Although RNA-seq is increasingly the technology of choice, a wealth of expression data already exist in the form of microarray data. If machine learning mo... View more
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