期刊
BIOINFORMATICS
卷 37, 期 24, 页码 4793-4800出版社
OXFORD UNIV PRESS
DOI: 10.1093/bioinformatics/btab565
关键词
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资金
- National Nature Science Foundation of China [81973244, U19A2067, 61772543, U1435222, 61625202, 61272056]
- National Key Research and Development Program of China [2017YFB0202602, 2018YFC0910405, 2017YFC1311003, 2016YFC1302500, 2016YFB0200400, 2017YFB0202104]
- Science Foundation for Distinguished Young Scholars of Hunan Province [2020JJ2009]
- Hunan Provincial Innovation Foundation For Postgraduate
- Fundamental Research Funds for the Central Universities [2016B090918122]
- Guangdong Provincial Department of Science and Technology [2016B090918122]
- Funds of Peng Cheng Lab, State Key Laboratory of Chemo/Biosensing and Chemometrics
This study proposes a BioHN-based self-supervised representation learning approach for entity relationship predictions, which effectively captures global structure and local associations through meta path detection mechanism and entity mask learning strategy, achieving superior performance on multiple datasets.
Motivation: Predicting entity relationship can greatly benefit important biomedical problems. Recently, a large amount of biomedical heterogeneous networks (BioHNs) are generated and offer opportunities for developing network-based learning approaches to predict relationships among entities. However, current researches slightly explored BioHNs-based self-supervised representation learning methods, and are hard to simultaneously capturing local- and global-level association information among entities. Results: In this study, we propose a BioHN-based self-supervised representation learning approach for entity relationship predictions, termed BioERP. A self-supervised meta path detection mechanism is proposed to train a deep Transformer encoder model that can capture the global structure and semantic feature in BioHNs. Meanwhile, a biomedical entity mask learning strategy is designed to reflect local associations of vertices. Finally, the representations from different task models are concatenated to generate two-level representation vectors for predicting relationships among entities. The results on eight datasets show BioERP outperforms 30 state-of-the-art methods. In particular, BioERP reveals great performance with results close to 1 in terms of AUC and AUPR on the drug-target interaction predictions. In summary, BioERP is a promising bio-entity relationship prediction approach.
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