Predicting Drug Sensitivity of Cancer Cells Lines via Collaborative Filtering with Contextual Attention.
Yifeng Tao, Shuangxia Ren, Michael Q. Ding, Russell Schwartz, Xinghua Lu. Predicting Drug Sensitivity of Cancer Cells Lines via Collaborative Filtering with Contextual Attention. Proceedings of Machine Learning Research. 2020. 1-A7
Accurate anti-cancer drug recommendations and the identiﬁcation of essential biomarkers for this task are crucial to precision oncology. Large-scale drug response assays on cancer cell lines provide a potential way to understand the interplay of drugs and cancer cells. In this work, we present CADRE (Contextual Attention-based Drug REsponse), a model that accurately infers the response of cancer cell lines to a panel of candidate compounds based on the omics proﬁles, such as gene expressions, of cancer cells. CADRE builds on the framework of collaborative ﬁltering, which provides robustness to the noise of biological data by leveraging similarities within drugs and cell lines. It utilizes the contextual attention mechanism to identify informative biomarkers of these cell lines, which boosts prediction accuracy and aﬀords interpretability of results. In addition, CADRE incorporates external knowledge of drug target pathways and co-expression patterns of genes to further improve feature representations and model performance. Comprehensive evaluations of CADRE and competing models on two large-scale pharmacogenomic datasets show its superiority in both prediction performance and interpretability. CADRE identiﬁes as vital biomarkers genes related to intracellular vesicles and signaling receptor binding, shedding light on its translational potential in the clinical practice of cancer treatment.