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Hu, E.Y. ; Oleshko, S. ; Firmani, S. ; Cheng, H.* ; Zhu, Z.* ; Ulmer, M.A. ; Arnold, M. ; Colomé-Tatché, M. ; Tang, J.* ; Xhonneux, S.* ; Marsico, A.

Enhancing link prediction in biomedical knowledge graphs with BioPathNet.

Nat. Bio. Eng., DOI: 10.1038/s41551-025-01598-z (2026)
Publ. Version/Full Text Research data DOI PMC
Open Access Hybrid
Creative Commons Lizenzvertrag
Understanding complex interactions in biomedical networks is crucial for advancements in biomedicine, but traditional link prediction (LP) methods are limited in capturing this complexity. We present BioPathNet, a graph neural network framework based on the neural Bellman-Ford network (NBFNet), addressing limitations of traditional representation-based learning methods through path-based reasoning for LP in biomedical knowledge graphs. Unlike node-embedding frameworks, BioPathNet learns representations between node pairs by considering all relations along paths, enhancing prediction accuracy and interpretability, and allowing visualization of influential paths and biological validation. BioPathNet leverages a background regulatory graph for enhanced message passing and uses stringent negative sampling to improve precision and scalability. BioPathNet outperforms or matches existing methods across diverse tasks including gene function annotation, drug-disease indication, synthetic lethality and lncRNA-target interaction prediction. Our study identifies promising additional drug indications for diseases such as acute lymphoblastic leukaemia and Alzheimer's disease, validated by medical experts and clinical trials. In addition, we prioritize putative synthetic lethal gene pairs and regulatory lncRNA-target interactions. BioPathNet's interpretability will enable researchers to trace prediction paths and gain molecular insights.
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Publication type Article: Journal article
Document type Scientific Article
Keywords Protein; Expression; Inhibitors; Pathway; Disease; Cells
ISSN (print) / ISBN 2157-846X
e-ISSN 2157-846X
Journal Nature biomedical engineering
Publisher Nature Publishing Group
Publishing Place London ; New York NY ; Tokyo
Reviewing status Peer reviewed
Institute(s) Institute of Computational Biology (ICB)
Grants Joint research school 'Munich School for Data Science (MUDS)'
Joachim Herz Foundation
National Institutes of Health/National Institute on Aging
BMBF Cluster4Future programme (Cluster for Nucleic Acid Therapeutics Munich, CNATM)
Helmholtz Zentrum Munchen - Deutsches Forschungszentrum fur Gesundheit und Umwelt (GmbH)

Helmholtz Association under CausalCellDynamics