title: Identifying genes related to drug anticancer mechanisms using support vector machine.
abstract: In an effort to identify genes related to the cell line 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 was used to label genes into any of the five predefined anticancer drug mechanistic categories. Among dozens of unequivocally categorized genes, many were known to be causally related to the drug mechanisms. For example, a few genes were found to be involved in the biological process triggered by the drugs (e.g. DNA polymerase epsilon was the direct target for the drugs from DNA antimetabolites category). DNA repair-related genes were found to be enriched for about eight-fold in the resulting gene set relative to the entire gene set. Some uncharacterized transcripts might be of interest in future studies. This method of correlating the drugs and genes provides a strategy for finding novel biologically significant relationships for molecular pharmacology.
authors.affiliation: Institute of Bioinformatics, Department of Biological Sciences and Biotechnology, Tsinghua University, Beijing, China.
authors.forename: Lei
authors.initials: L
authors.lastname: Bao
authors.forename: Zhirong
authors.initials: Z
authors.lastname: Sun
mesh_terms: D000970:Antineoplastic Agents / Q000494:pharmacology*; D002460:Cell Line; D015870:Gene Expression; D005822:Genetic Vectors; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods
chemical_list: D000970:Antineoplastic Agents