GraphCDR: a graph neural network method with contrastive learning for cancer drug response prediction - PubMed (original) (raw)

. 2022 Jan 17;23(1):bbab457.

doi: 10.1093/bib/bbab457.

Affiliations

GraphCDR: a graph neural network method with contrastive learning for cancer drug response prediction

Xuan Liu et al. Brief Bioinform. 2022.

Abstract

Predicting the response of a cancer cell line to a therapeutic drug is an important topic in modern oncology that can help personalized treatment for cancers. Although numerous machine learning methods have been developed for cancer drug response (CDR) prediction, integrating diverse information about cancer cell lines, drugs and their known responses still remains a great challenge. In this paper, we propose a graph neural network method with contrastive learning for CDR prediction. GraphCDR constructs a graph neural network based on multi-omics profiles of cancer cell lines, the chemical structure of drugs and known cancer cell line-drug responses for CDR prediction, while a contrastive learning task is presented as a regularizer within a multi-task learning paradigm to enhance the generalization ability. In the computational experiments, GraphCDR outperforms state-of-the-art methods under different experimental configurations, and the ablation study reveals the key components of GraphCDR: biological features, known cancer cell line-drug responses and contrastive learning are important for the high-accuracy CDR prediction. The experimental analyses imply the predictive power of GraphCDR and its potential value in guiding anti-cancer drug selection.

Keywords: Cancer drug response prediction; Contrastive learning; Drug structure; Graph neural network; Multi-omics.

© The Author(s) 2021. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

PubMed Disclaimer

Similar articles

Cited by

Publication types

MeSH terms

Substances

LinkOut - more resources