title: Recursive Random Lasso (RRLasso) for Identifying Anti-Cancer Drug Targets.
abstract: Uncovering driver genes is crucial for understanding heterogeneity in cancer. L1-type regularization approaches have been widely used for uncovering cancer driver genes based on genome-scale data. Although the existing methods have been widely applied in the field of bioinformatics, they possess several drawbacks: subset size limitations, erroneous estimation results, multicollinearity, and heavy time consumption. We introduce a novel statistical strategy, called a Recursive Random Lasso (RRLasso), for high dimensional genomic data analysis and investigation of driver genes. For time-effective analysis, we consider a recursive bootstrap procedure in line with the random lasso. Furthermore, we introduce a parametric statistical test for driver gene selection based on bootstrap regression modeling results. The proposed RRLasso is not only rapid but performs well for high dimensional genomic data analysis. Monte Carlo simulations and analysis of the "Sanger Genomics of Drug Sensitivity in Cancer dataset from the Cancer Genome Project" show that the proposed RRLasso is an effective tool for high dimensional genomic data analysis. The proposed methods provide reliable and biologically relevant results for cancer driver gene selection.
authors.affiliation: Human Genome Center, Institute of Medical Science, University of Tokyo, Tokyo, Japan.
authors.forename: Heewon
authors.initials: H
authors.lastname: Park
authors.affiliation: Human Genome Center, Institute of Medical Science, University of Tokyo, Tokyo, Japan.
authors.forename: Seiya
authors.initials: S
authors.lastname: Imoto
authors.affiliation: Human Genome Center, Institute of Medical Science, University of Tokyo, Tokyo, Japan.
authors.forename: Satoru
authors.initials: S
authors.lastname: Miyano
mesh_terms: D000970:Antineoplastic Agents / Q000494:pharmacology* / Q000627:therapeutic use; D019295:Computational Biology / Q000379:methods*; D023281:Genomics; D058990:Molecular Targeted Therapy; D009010:Monte Carlo Method; D009369:Neoplasms / Q000188:drug therapy* / Q000235:genetics
chemical_list: D000970:Antineoplastic Agents