A matrix completion method for drug response prediction in personalized medicine

One of the significant goals of personalized medicine is to provide the right treatment to patients based on their molecular features. Several big projects have been launched and generated a large amount of -omics and drug response data for cell lines of the human. These projects are very useful for testing of drug responses on cell lines before employing clinical trials on humans. However, a range of drugs and cell lines have not been tested yet. Thus, many computational methods attempt to predict such the responses to maximize the treatment efficiency and to minimize side-effects. These methods use not only known drug -- cell lines responses but also the similarity between drugs and between cell lines. Nevertheless, -omics data for cell lines which is used to calculate the cell-line similarities usually varies among platforms leading to heterogeneous results. Therefore, in this study, we propose a drug response prediction method (MCDRP) based on a matrix completion technique using only known drug -- cell lines response information to predict drug responses for untested cell lines. The method can impute responses for not only one at time but also all drugs simultaneously. In comparison with other methods, we found that our method achieved better performance for IC50 response measurement.