Drug Sensitivity Prediction for Cancer Cell Lines Based on Pairwise Kernels and Mirna Profiles

Abstract

Cancer cell lines comprise an important tool to design and evaluate new drug candidates. Prediction of in vivo drug response for cancer cell lines has become attractive due to recently issued large scale drug screen databases. The data provided by these databases can be the key to model drug sensitivity for cancer cell lines. The data provided by these databases is in the form of drug cell line pairs where a natural method for prediction of drug response, therefore is pairwise support vector machines. This paper presents results on the application of pairwise kernels for drug response prediction, where the results are promising compared to some previously well-performed methods on this task. In addition, effect of exploiting microRNA profiles of cancer cell lines together with mRNA profiles is given.