# Predicting Biomedical Interactions with Higher-Order Graph Convolutional Networks

arXiv: https://arxiv.org/abs/2010.08516 · HTML: https://mlsanity.com/paper/2010.08516/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Oct 2020.

## Built on by (most cited first, 7 of 7)

- [Line Graph Contrastive Learning for Link Prediction](https://mlsanity.com/paper/2210.13795/) (2022)
- [Comprehensive evaluation of deep and graph learning on drug-drug interactions prediction](https://mlsanity.com/paper/2306.05257/) (2023)
- [Learning to Denoise Biomedical Knowledge Graph for Robust Molecular Interaction Prediction](https://mlsanity.com/paper/2312.06682/) (2023)
- [ResMGCN: Residual Message Graph Convolution Network for Fast Biomedical Interactions Discovering](https://mlsanity.com/paper/2311.07632/) (2023)
- [Predicting Biomedical Interactions with Probabilistic Model Selection for Graph Neural Networks](https://mlsanity.com/paper/2211.13231/) (2022)
- [Bayesian Neighborhood Adaptation for Graph Neural Networks](https://mlsanity.com/paper/2602.05358/) (2026)
- [GLaDiGAtor: Language-Model-Augmented Multi-Relation Graph Learning for Predicting Disease-Gene Associations](https://mlsanity.com/paper/2602.18769/) (2026)

## Abstract (arXiv)

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.