OMNI: OptimizedMultiview Network Integration withHeterogeneous Graph Attention for Biomedical Interaction Prediction

Accurate prediction of biomedical relationships, such as chemical-gene interactions, is fundamental to understanding disease mechanisms and advancing drug discovery. With the rapid growth of heterogeneous biological data, modeling large-scale, multientity networks has become increasingly challenging. Traditional approaches, including homogeneous GNNs (e.g., GCN and GAT) and meta-path-based random walks, struggle to efficiently capture high-order, diverse neighborhood information in complex biomedical graphs. To address these limitations, we apply a multiview heterogeneous graph attention network (GAT)-based architecture that effectively aggregates rich, heterogeneous interactions across multiple biomedical entity types. The proposed encoder captures comprehensive structural and semantic information while remaining computationally efficient. Through optimized aggregation strategies and multiprocessing, the model generates high-quality node embeddings with significantly reduced training time. For relation prediction, multiple decoder architectures were evaluated, with a multilayer perceptron (MLP) identified as the most effective for accurate multitype relation classification. The resulting network comprises 124,604 unique nodes and 48,482,286 interactions. Experimental results show that the proposed model consistently outperforms state-of-the-art methods, including CGINet, Node2Vec, HAN, HGT, and the GCN-based BioNet, achieving an AUROC of 0.90 for chemical-gene interaction prediction. The model further explores its ability to identify top-ranking chemical-gene interactions in cancer and to predict gene-phytochemical relationships. Overall, this work introduces a scalable and powerful framework for biomedical relation prediction, with strong potential applications in drug screening and disease mechanism discovery.