title: Class prediction in toxicogenomics.
abstract: The intent of this article is to discuss some of the complexities of toxicogenomics data and the statistical design and analysis issues that arise in the course of conducting a toxicogenomics study. We also describe a procedure for classifying compounds into various hepatotoxicity classes based on gene expression data. The methodology involves first classifying a compound as toxic or nontoxic and subsequently classifying the toxic compounds into the hepatotoxicity classes, based on votes by binary classifiers. The binary classifiers are constructed by using genes selected to best elicit differences between the two classes. We show that the gene selection strategy improves the misclassification error rates and also delivers gene pathways that exhibit biological relevance.
authors.affiliation: Department of Non-Clinical Biostatistics, Johnson and Johnson Pharmaceutical Research and Development, LLC, Raritan, New Jersey 08869, USA. nraghava@prdus.jnj.com
authors.forename: Nandini
authors.initials: N
authors.lastname: Raghavan
authors.forename: Dhammika
authors.initials: D
authors.lastname: Amaratunga
authors.forename: Alex Y
authors.initials: AY
authors.lastname: Nie
authors.forename: Michael
authors.initials: M
authors.lastname: McMillian
mesh_terms: D000465:Algorithms; D056486:Chemical and Drug Induced Liver Injury / Q000235:genetics; D003627:Data Interpretation, Statistical; D016002:Discriminant Analysis; D015870:Gene Expression; D016014:Linear Models; D020411:Oligonucleotide Array Sequence Analysis / Q000706:statistics & numerical data; D011237:Predictive Value of Tests; D012333:RNA, Messenger / Q000096:biosynthesis / Q000235:genetics; D043922:Toxicogenetics / Q000145:classification / Q000706:statistics & numerical data*
chemical_list: D012333:RNA, Messenger