title: Recursive partitioning for tumor classification with gene expression microarray data.
abstract: Precise classification of tumors is critically important for cancer diagnosis and treatment. It is also a scientifically challenging task. Recently, efforts have been made to use gene expression profiles to improve the precision of classification, with limited success. Using a published data set for purposes of comparison, we introduce a methodology based on classification trees and demonstrate that it is significantly more accurate for discriminating among distinct colon cancer tissues than other statistical approaches used heretofore. In addition, competing classification trees are displayed, which suggest that different genes may coregulate colon cancers.
authors.affiliation: Department of Epidemiology and Public Health, Yale University School of Medicine, New Haven, CT 06520-8034, USA. heping.zhang@yale.edu
authors.forename: H
authors.initials: H
authors.lastname: Zhang
authors.forename: C Y
authors.initials: CY
authors.lastname: Yu
authors.forename: B
authors.initials: B
authors.lastname: Singer
authors.forename: M
authors.initials: M
authors.lastname: Xiong
mesh_terms: D020869:Gene Expression Profiling; D006801:Humans; D009369:Neoplasms / Q000145:classification* / Q000235:genetics; D020411:Oligonucleotide Array Sequence Analysis