title: Classification methods for the development of genomic signatures from high-dimensional data.
abstract: Personalized medicine is defined by the use of genomic signatures of patients to assign effective therapies. We present Classification by Ensembles from Random Partitions (CERP) for class prediction and apply CERP to genomic data on leukemia patients and to genomic data with several clinical variables on breast cancer patients. CERP performs consistently well compared to the other classification algorithms. The predictive accuracy can be improved by adding some relevant clinical/histopathological measurements to the genomic data.
authors.affiliation: Division of Biometry and Risk Assessment, National Center for Toxicological Research, FDA, NCTR Road, Jefferson, AR 72079, USA. hojin.moon@fda.hhs.gov
authors.forename: Hojin
authors.initials: H
authors.lastname: Moon
authors.forename: Hongshik
authors.initials: H
authors.lastname: Ahn
authors.forename: Ralph L
authors.initials: RL
authors.lastname: Kodell
authors.forename: Chien-Ju
authors.initials: CJ
authors.lastname: Lin
authors.forename: Songjoon
authors.initials: S
authors.lastname: Baek
authors.forename: James J
authors.initials: JJ
authors.lastname: Chen
mesh_terms: D000465:Algorithms; D001943:Breast Neoplasms / Q000145:classification / Q000235:genetics*; D015894:Genome, Human; D006801:Humans; D007938:Leukemia / Q000145:classification / Q000235:genetics*; D011379:Prognosis