Heterogeneous Graph based Deep Learning for Biomedical Network Link Prediction

Jinjiang Guo, Jie Li, Dawei Leng, Lurong Pan

Multi-scale biomedical knowledge networks are expanding with emerging experimental technologies. Link prediction is increasingly used especially in bipartite biomedical networks. We propose a Graph Neural Networks (GNN) method, namely Graph Pair based Link Prediction model (GPLP), for predicting biomedical network links simply based on their topological interaction information. In GPLP, 1-hop subgraphs extracted from known network interaction matrix is learnt to predict missing links. To evaluate our method, three heterogeneous biomedical networks were used, i.e. Drug-Target Interaction network (DTI), Compound-Protein Interaction network (CPI) from NIH Tox21, and Compound-Virus Inhibition network (CVI). In 5-fold cross validation, our proposed GPLP method significantly outperforms over the state-of-the-art baselines. Besides, robustness is tested with different network incompleteness. Our method has the potential applications in other biomedical networks.

Knowledge Graph



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