title: Random forests for microarrays.
abstract: Random Forests is a powerful multipurpose tool for predicting and understanding data. If gene expression data come from known groups or classes (e.g., tumor patients and controls), Random Forests can rank the genes in terms of their usefulness in separating the groups. When the groups are unknown, Random Forests uses an intrinsic measure of the similarity of the genes to extract useful multivariate structure, including clusters. This chapter summarizes the Random Forests methodology and illustrates its use on freely available data sets.
authors.affiliation: Department of Mathematics and Statistics, Utah State University, Logan, UT, USA.
authors.forename: Adele
authors.initials: A
authors.lastname: Cutler
authors.forename: John R
authors.initials: JR
authors.lastname: Stevens
mesh_terms: D000818:Animals; D003627:Data Interpretation, Statistical; D006801:Humans; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods* / Q000706:statistics & numerical data*; D009626:Terminology as Topic