Heterogeneous Graph based Deep Learning for Biomedical Network Link Prediction
This work addresses the problem of identifying hidden biological interactions in biomedical networks for researchers, but it is incremental as it builds on existing GNN approaches.
The authors tackled link prediction in biomedical networks by proposing a Graph Neural Networks method called GPLP, which uses 1-hop subgraphs from topological interaction information, and it significantly outperformed state-of-the-art baselines on three heterogeneous biomedical networks.
Multi-scale biomedical knowledge networks are expanding with emerging experimental technologies that generates multi-scale biomedical big data. Link prediction is increasingly used especially in bipartite biomedical networks to identify hidden biological interactions and relationshipts between key entities such as compounds, targets, gene and diseases. 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). Our proposed GPLP method significantly outperforms over the state-of-the-art baselines. In addition, different network incompleteness is analysed with our devised protocol, and we also design an effective approach to improve the model robustness towards incomplete networks. Our method demonstrates the potential applications in other biomedical networks.