Improve Glioblastoma Multiforme Prognosis Prediction by Using Feature Selection and Multiple Kernel Learning.

Glioblastoma multiforme (GBM) is a highly aggressive type of brain cancer with very low median survival. In order to predict the patient's prognosis, researchers have proposed rules to classify different glioma cancer cell subtypes. However, survival time of different subtypes of GBM is often various due to different individual basis. Recent development in gene testing has evolved classic subtype rules to more specific classification rules based on single biomolecular features. These classification methods are proven to perform better than traditional simple rules in GBM prognosis prediction. However, the real power behind the massive data is still under covered. We believe a combined prediction model based on more than one data type could perform better, which will contribute further to clinical treatment of GBM. The Cancer Genome Atlas (TCGA) database provides huge dataset with various data types of many cancers that enables us to inspect this aggressive cancer in a new way. In this research, we have improved GBM prognosis prediction accuracy further by taking advantage of the minimum redundancy feature selection method (mRMR) and Multiple Kernel Machine (MKL) learning method. Our goal is to establish an integrated model which could predict GBM prognosis with high accuracy.