title: Identifying genes related to chemosensitivity using support vector machine.
abstract: In an effort to identify genes involved in chemosensitivity and to evaluate the functional relationships between genes and anticancer drugs acting by the same mechanism, a supervised machine learning approach called support vector machine (SVM) is used to associate genes with any of five predefined anticancer drug mechanistic categories. The drug activity profiles are used as training examples to train the SVM and then the gene expression profiles are used as test examples to predict their associated mechanistic categories. This method of correlating drugs and genes provides a strategy for finding novel biologically significant relationships for molecular pharmacology.
authors.affiliation: Department of Molecular Sciences, The University of Tennessee Health Science Center, Memphis, USA.
authors.forename: Lei
authors.initials: L
authors.lastname: Bao
mesh_terms: D000465:Algorithms; D000970:Antineoplastic Agents / Q000494:pharmacology*; D001185:Artificial Intelligence; D019295:Computational Biology; D004247:DNA / Q000187:drug effects*; D004249:DNA Damage; D004260:DNA Repair; D004354:Drug Screening Assays, Antitumor; D020869:Gene Expression Profiling; D015972:Gene Expression Regulation, Neoplastic; D006801:Humans; D015233:Models, Statistical; D020411:Oligonucleotide Array Sequence Analysis; D010363:Pattern Recognition, Automated
chemical_list: D000970:Antineoplastic Agents; D004247:DNA