Integration of Clinical Information and Gene Expression Profiles for Prediction of Chemo-Response for Ovarian Cancer

摘要

Ovarian cancer is the fifth leading cause of cancer death among women in the United States and western Europe. Platinum drugs are the most active agents in epithelial ovarian cancer therapy. In order to improve the prediction of response to platinum-based chemotherapy for advanced-stage ovarian cancers, we describe an integrated model which combines clinical information tumor and treatment information, with gene expression profile. This integrated modeling framework is based on the support vector machine classifier that evaluates the contributions of both clinical and gene expression data. The results show that the integrated model combining clinical information and gene expression profiles improve the prediction accuracy compared to those made by using gene expression predictor alone