Selection of Patient Samples and Genes for Outcome Prediction

Gene expression profiles with clinical outcome data enable monitoring of disease progression and prediction of patient survival at the molecular level. We present a new computational method for outcome prediction. Our idea is to use an informative subset of original training samples. This subset consists of only short-term survivors who died within a short period and long-term survivors who were still alive after a long follow-up time. These extreme training samples yield a clear platform to identify genes whose expression is related to survival. To find relevant genes, we combine two feature selection methods -- entropy measure and Wilcoxon rank sum test -- so that a set of sharp discriminating features are identified. The selected training samples and genes are then integrated by a support vector machine to build a prediction model, by which each validation sample is assigned a survival/relapse risk score for drawing Kaplan-Meier survival curves. We apply this method to two data set...

• Type: Conference
• Year: 2004
• Where: CSB
• Authors: Huiqing Liu, Jinyan Li, Limsoon Wong
• Keywords: Bioinformatics, CSB 2004, Gene Expression Profiles, Risk Scores, Training Samples