Predicting Drug Sensitivity of Cancer Cell Lines via Collaborative Filtering with Contextual Attention

Yifeng Tao, Shuangxia Ren, Michael Q. Ding, Russell Schwartz, Xinghua Lu
; Proceedings of the 5th Machine Learning for Healthcare Conference, PMLR 126:660-684, 2020.

Abstract

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.

Cite this Paper


BibTeX
@InProceedings{pmlr-v126-tao20a, title = {Predicting Drug Sensitivity of Cancer Cell Lines via Collaborative Filtering with Contextual Attention}, author = {Tao, Yifeng and Ren, Shuangxia and Ding, Michael Q. and Schwartz, Russell and Lu, Xinghua}, booktitle = {Proceedings of the 5th Machine Learning for Healthcare Conference}, pages = {660--684}, year = {2020}, editor = {Finale Doshi-Velez and Jim Fackler and Ken Jung and David Kale and Rajesh Ranganath and Byron Wallace and Jenna Wiens}, volume = {126}, series = {Proceedings of Machine Learning Research}, address = {Virtual}, month = {07--08 Aug}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v126/tao20a/tao20a.pdf}, url = {http://proceedings.mlr.press/v126/tao20a.html}, abstract = {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.} }
Endnote
%0 Conference Paper %T Predicting Drug Sensitivity of Cancer Cell Lines via Collaborative Filtering with Contextual Attention %A Yifeng Tao %A Shuangxia Ren %A Michael Q. Ding %A Russell Schwartz %A Xinghua Lu %B Proceedings of the 5th Machine Learning for Healthcare Conference %C Proceedings of Machine Learning Research %D 2020 %E Finale Doshi-Velez %E Jim Fackler %E Ken Jung %E David Kale %E Rajesh Ranganath %E Byron Wallace %E Jenna Wiens %F pmlr-v126-tao20a %I PMLR %J Proceedings of Machine Learning Research %P 660--684 %U http://proceedings.mlr.press %V 126 %W PMLR %X 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.
APA
Tao, Y., Ren, S., Ding, M.Q., Schwartz, R. & Lu, X.. (2020). Predicting Drug Sensitivity of Cancer Cell Lines via Collaborative Filtering with Contextual Attention. Proceedings of the 5th Machine Learning for Healthcare Conference, in PMLR 126:660-684

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