Predicting microbe–disease association based on graph autoencoder and inductive matrix completion with multi-similarities fusion
The proposed GIMMDA model show a strong capability in predicting microbe-disease associations. We expect that GPUDMDA will help identify potential microbe-related diseases in the future.
Authors
- Qiaohui Liu
- Kai Shi (ORCID: https://orcid.org/0000-0003-0038-625X)
- Yi Zhang (ORCID: https://orcid.org/0000-0001-7201-2092)
- Li Lin (ORCID: https://orcid.org/0000-0003-2783-1114)
- Kai Huang (ORCID: https://orcid.org/0000-0002-7121-0318)
- Huilin Zheng (ORCID: https://orcid.org/0000-0003-3845-6612)
Institutions
- Guilin University of Technology (CN)
Publication Details
- Journal
- Frontiers in Microbiology
- Published
- 2024-09-06
- DOI
- https://doi.org/10.3389/fmicb.2024.1438942
- Citations
- 4
- Primary Topic
- Bioinformatics and Genomic Networks
- Type
- article
- Field-Weighted Citation Impact
- 0.94
Funders
- National Natural Science Foundation of China
- Guilin University of Technology
- Division of Graduate Education