abstract = {The medical research facilitates to acquire a diverse type of data from
the same individual for a 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 highthroughput
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.},
author = {Thomas, Jaya and Sael, Lee},
journal = {International Journal of Data Mining and Bioinformatics},
title = {{Multi-Kernel LS-SVM Based Integration Bio-Clinical
Data Analysis and Application to Ovarian Cancer}},
year = {2017},
issue = {2},
pages = {150--167}