Published in

Oxford University Press (OUP), Briefings in Bioinformatics, 1(8), p. 32-44

DOI: 10.1093/bib/bbl016

Links

Tools

Export citation

Search in Google Scholar

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

Journal article published in 2006 by Al-L. Boulesteix, Korbinian Strimmer
This paper is made freely available by the publisher.
This paper is made freely available by the publisher.

Full text: Download

Green circle
Preprint: archiving allowed
Upload
Green circle
Postprint: archiving allowed
Upload
Red circle
Published version: archiving forbidden
Policy details
Data provided by SHERPA/RoMEO

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.