ArticleComputational and structural biotechnology journal2024
Curvature-enhanced graph convolutional network for biomolecular interaction prediction.
Article in Computational and structural biotechnology journal, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- KG-DFI: A Prediction of Drug-Food Interactions Based on Knowledge Graph Embedding.Computational and structural biotechnology journal · 2026Article
- GADRC: a graph-based approach for drug repositioning with deep residual networks and computational feature-guided undersampling.Journal of computer-aided molecular design · 2025Article
- Emerging regulatory mechanisms and functions of biomolecular condensates: implications for therapeutic targets.Signal transduction and targeted therapy · 2025Review
- GraphADT: empowering interpretable predictions of acute dermal toxicity with multi-view graph pooling and structure remapping.Bioinformatics (Oxford, England) · 2024Article
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Authors and funding
6 authors.
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Abstract
Geometric deep learning has demonstrated a great potential in non-Euclidean data analysis. The incorporation of geometric insights into learning architecture is vital to its success. Here we propose a curvature-enhanced graph convolutional network (CGCN) for biomolecular interaction prediction. Our CGCN employs Ollivier-Ricci curvature (ORC) to characterize network local geometric properties and enhance the learning capability of GCNs. More specifically, ORCs are evaluated based on the local topology from node neighborhoods, and further incorporated into the weight function for the feature aggregation in message-passing procedure. Our CGCN model is extensively validated on fourteen real-world bimolecular interaction networks and analyzed in details using a series of well-designed simulated data. It has been found that our CGCN can achieve the state-of-the-art results. It outperforms all existing models, as far as we know, in thirteen out of the fourteen real-world datasets and ranks as the second in the rest one. The results from the simulated data show that our CGCN model is superior to the traditional GCN models regardless of the positive-to-negative-curvature ratios, network densities, and network sizes (when larger than 500).
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.