DIMDRP: A Double Iteration Method for Drug Response Prediction

Abstract

Using molecular profiles to predict the drug response is of practical importance in precision medicine and have been extensively studied. Due to the complexity of molecular information, the existing methods couldn't capture enough information and their prediction performances are not satisfying. In this study, we propose a method called DIMDRP (double iteration method for drug response prediction) which improves a lot in the prediction accuracy. DIMDRP integrates several important molecular information including miRNA expression, drug chemical structure, target interaction, drug-target interaction and cell line-drug response, and constructs a heterogeneous network. Then an improved information flow iteration algorithm is used to calculate association scores of cell line-drug responses, and we prioritize the cell lines for each query drug. The cross-validation experiments show that the average area under curve (AUC) of DIMDRP is as high as 0.8953, and two other measurement metrics are applied to assess the performance. When compared to other approaches, our method shows a significant advantage for all metrics. We also consider the specific tissue condition into our competition from a practical aspect. Last, a case is studied to predict novel cell line-drug responses, some of which can be evidenced by previous experiments.

Keywords

• Drug response
• information flow
• information integration
• iteration
• topology structure