CGINet: graph convolutional network-based model for identifying chemical-gene interaction in an integrated multi-relational graph.

Journal: BMC bioinformatics

PMID: 33243142

Abstract

BACKGROUND: Elucidation of interactive relation between chemicals and genes is of key relevance not only for discovering new drug leads in drug development but also for repositioning existing drugs to novel therapeutic targets. Recently, biological network-based approaches have been proven to be effective in predicting chemical-gene interactions.

Authors

Wei Wang

State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, Macau 999078, China.
Xi Yang

Department of Health Outcomes and Biomedical Informatics.
Chengkun Wu

School of Computer Science, National University of Defense Technology, Changsha, 410073, China. Chenkun_wu@nudt.edu.cn.
Canqun Yang

School of Computer Science, National University of Defense Technology, Changsha, 410073, China.

Keywords

Algorithms Area Under Curve Epistasis, Genetic Gene Regulatory Networks Humans Models, Genetic Neural Networks, Computer

External Resources

View on PubMed Access via DOI PubMed (33243142)

CGINet: graph convolutional network-based model for identifying chemical-gene interaction in an integrated multi-relational graph.

Abstract

Authors

Keywords

External Resources

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