A lightweight single-view contrastive learning hypergraph neural network for food–microbe–disease association prediction
A novel computational model, LSCHNN, is proposed, marking the first integration of hypergraph neural networks with lightweight single-view contrastive learning for ternary FMD association prediction, providing a groundbreaking framework for precision nutrition and personalized dietary interventions.
Authors
- Jinlin Zhu (ORCID: https://orcid.org/0000-0002-4296-5914)
- Yuchen Gao (ORCID: https://orcid.org/0000-0001-5914-0639)
- Dexian Huang (ORCID: https://orcid.org/0009-0002-9598-602X)
- Jian-Qiang Hu (ORCID: https://orcid.org/0000-0003-2989-8048)
- Mingyi Hu (ORCID: https://orcid.org/0000-0002-3927-0281)
- Baolong Wang (ORCID: https://orcid.org/0000-0003-0163-7575)
- Shixin Gu
- Y.S. Wu (ORCID: https://orcid.org/0009-0004-2625-8875)
- S. M. Mu
Institutions
- Jiangnan University (CN)
- State Key Laboratory of Food Science and Technology (CN)
Publication Details
- Journal
- BMC Bioinformatics
- Published
- 2025-11-04
- DOI
- https://doi.org/10.1186/s12859-025-06283-1
- Primary Topic
- Gut microbiota and health
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Natural Science Foundation of China
- Fundamental Research Funds for the Central Universities