Partial least squares: a versatile tool for the analysis of high-dimensional genomic data

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ID: 305579
2006
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Abstract
Partial least squares (PLS) is an efficient statistical regression technique that is highly suited for the analysis of genomic and proteomic data. In this article, we review both the theory underlying PLS as well as a host of bioinformatics applications of PLS. In particular, we provide a systematic comparison of the PLS approaches currently employed, and discuss analysis problems as diverse as, e.g. tumor classification from transcriptome data, identification of relevant genes, survival analysis and modeling of gene networks and transcription factor activities.
Reference Key
openalex_W2097057782 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Anne‐Laure Boulesteix, Korbinian Strimmer
Journal Briefings in bioinformatics
Year 2006
DOI
10.1093/bib/bbl016
URL
https://academic.oup.com/bib/article-pdf/8/1/32/737013/bbl016.pdf
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