  The medical research facilitates to acquire a diverse type of data from the
same individual for particular cancer. Recent studies show that utilizing such
diverse data results in more accurate predictions. The major challenge faced is
how to utilize such diverse data sets in an effective way. In this paper, we
introduce a multiple kernel based pipeline for integrative analysis of
high-throughput molecular data (somatic mutation, copy number alteration, DNA
methylation and mRNA) and clinical data. We apply the pipeline on Ovarian
cancer data from TCGA. After multiple kernels have been generated from the
weighted sum of individual kernels, it is used to stratify patients and predict
clinical outcomes. We examine the survival time, vital status, and neoplasm
cancer status of each subtype to verify how well they cluster. We have also
examined the power of molecular and clinical data in predicting dichotomized
overall survival data and to classify the tumor grade for the cancer samples.
It was observed that the integration of various data types yields higher
log-rank statistics value. We were also able to predict clinical status with
higher accuracy as compared to using individual data types.
