Drug Response Prediction by Globally Capturing Drug and Cell Line Information in a Heterogeneous Network. Issue 18 (14th September 2018) - Le, Duc-Hau; Pham, Van-Huy
Abstract: One of the most important problem in personalized medicine research is to precisely predict the drug response for each patient. Due to relationships between drugs, recent machine learning-based methods have solved this problem using multi-task learning models. However, chemical relationships between drugs have not been considered. In addition, using very high dimensions of -omics data (e.g., genetic variant and gene expression) also limits the prediction power. A recent dual-layer network-based method was proposed to overcome these limitations by embedding gene expression features into a cell line similarity network and drug relationships in a chemical structure-based drug similarity network. However, this method only considered neighbors of a query drug and a cell line. Previous studies also reported that genetic variants are less informative to predict an outcome than gene expression. Here, we develop a novel network-based method, named GloNetDRP, to overcome these limitations. Besides gene expression, we used the genetic variant to build another cell line similarity network. First, we constructed a heterogeneous network of drugs and cell lines by connecting a drug similarity network and a cell line similarity network by known drug–cell line responses. Then, we proposed a method to predict the responses by exploiting not only the neighbors but also other drugs and cell lines in the heterogeneous network. Experimental results on two large-scale cell line data sets show that prediction performance of GloNetDRP on gene expression and genetic variant data is comparable. In addition, GloNetDRP outperformed dual-layer network- and typical multi-task learning-based methods. Graphical Abstract: Highlights: Computational methods to predict drug response in personalized medicine are needed. Considering the relationship between drugs and cell lines globally improves prediction. Embedding mutations into cell line similarity networks makes genetic variant data more informative. Our method (GloNetDRP) achieves relatively high performance on both types of -omics data. GloNetDRP outperforms a locally network-based and a multi-task learning method.