# Multi-Kernel LS-SVM Based Bio-Clinical Data Integration: Applications to Ovarian Cancer

## TL;DR

This paper presents a multi-kernel LS-SVM approach for integrating diverse bio-clinical data to improve prediction and stratification in ovarian cancer, demonstrating enhanced accuracy over single data type analyses.

## Contribution

It introduces a novel multi-kernel pipeline for integrating molecular and clinical data, improving patient stratification and prediction in ovarian cancer.

## Key findings

- Higher log-rank statistics with integrated data
- Improved clinical status prediction accuracy
- Effective patient clustering based on multi-omics data

## Abstract

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.