Feature selection and molecular classification of cancer using genetic programming.

ABSTRACT: Despite important advances in microarray-based molecular classification of tumors, its application in clinical settings remains formidable. This is in part due to the limitation of current analysis programs in discovering robust biomarkers and developing classifiers with a practical set of genes. Genetic programming (GP) is a type of machine learning technique that uses evolutionary algorithm to simulate natural selection as well as population dynamics, hence leading to simple and comprehensible classifiers. Here we applied GP to cancer expression profiling data to select feature genes and build molecular classifiers by mathematical integration of these genes. Analysis of thousands of GP classifiers generated for a prostate cancer data set revealed repetitive use of a set of highly discrim

SUBMITTER: Yu J 

PROVIDER: S-EPMC1854845 | biostudies-literature | 2007 Apr

REPOSITORIES: biostudies-literature