biostudies-literatureYu JNIDA NIH HHSNCI NIH HHSNIGMS NIH HHS292-303https://www.ebi.ac.uk/biostudies/studies/S-EPMC1854845biostudies-literatureUnknown9(4)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 discrimNeoplasia (New York, N.Y.)Feature selection and molecular classification of cancer using genetic programming.PMC1854845R01 GM072007U54 DA021519-01A1U01 CA111275R01 CA097063U54 DA021519P30 CA046592P50 CA069568R01 CA970635P30 CA46592P50CA69568R01 GM072007-04GM 72007Ghosh DAlmal AAWorzel WPYu JDhanasekaran SMChinnaiyan AMfalseFeature selection and molecular classification of cancer using genetic programming.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 discrim2007-01-01T00:00:00Z2007 Apr2026-03-16T16:33:51.756Z2019-03-27T02:03:47ZS-EPMC18548451746077310.1593/neo.07121