Predicting Biomedical Interactions with Higher-Order Graph Convolutional Networks

TL;DR

This paper introduces HOGCN, a higher-order graph convolutional network that aggregates information from multiple neighborhood distances to improve biomedical interaction prediction accuracy and robustness.

Contribution

The paper presents a novel higher-order GCN model that considers multiple neighborhood distances, enhancing biomedical interaction prediction over existing methods.

Findings

HOGCN outperforms existing models on four interaction networks.

HOGCN maintains accuracy on noisy, sparse networks.

Validated novel interactions through literature case studies.

Abstract

Biomedical interaction networks have incredible potential to be useful in the prediction of biologically meaningful interactions, identification of network biomarkers of disease, and the discovery of putative drug targets. Recently, graph neural networks have been proposed to effectively learn representations for biomedical entities and achieved state-of-the-art results in biomedical interaction prediction. These methods only consider information from immediate neighbors but cannot learn a general mixing of features from neighbors at various distances. In this paper, we present a higher-order graph convolutional network (HOGCN) to aggregate information from the higher-order neighborhood for biomedical interaction prediction. Specifically, HOGCN collects feature representations of neighbors at various distances and learns their linear mixing to obtain informative representations of biomedical entities. Experiments on four interaction networks, including protein-protein, drug-drug, drug-target, and gene-disease interactions, show that HOGCN achieves more accurate and calibrated predictions. HOGCN performs well on noisy, sparse interaction networks when feature representations of neighbors at various distances are considered. Moreover, a set of novel interaction predictions are validated by literature-based case studies.