Machine Learning Models For Classification Of Lung Cancer and Selection of Genomic Markers Using Array Gene Expression Data

This research explores machine learning methods for the development of computer models that use gene expression data to distinguish between tumor and non-tumor, between metastatic and non-metastatic, and between histological subtypes of lung cancer. A second goal is to identify small sets of gene predictors and study their properties in terms of stability, size, and biological interpretation. We apply four classifier and two gene selection algorithms to a 12,600 oligonucleotide array dataset from 203 patients and normal human subjects. The resulting models exhibit excellent classification performance. Detailed analysis of the results reveals several interesting computational and biomedical research directions.