Prediction of microbe-drug associations using a CNN-Bernoulli random forest model
The model was evaluated using five-fold cross-validation on the Microbe-Drug Association Database (MDAD) and abiofilm datasets. CNNBRFMDA achieved mean AUC scores of 0.9017 ± 0.0032 (MDAD) and 0.9146 ± 0.0041 (abiofilm). Two case studies further validated the model's reliability: 41 of the top 50 predicted microbes associated with ciprofloxacin and 38 of the top 50 associated with moxifloxacin were confirmed through literature review.
Authors
- Qinggang Bu
- Jia Qu (ORCID: https://orcid.org/0000-0002-2611-8731)
- Zekang Bian (ORCID: https://orcid.org/0000-0002-0512-9247)
- Q. Y. Li
- Zihao Song (ORCID: https://orcid.org/0000-0002-3650-9330)
- Jincheng Zhao (ORCID: https://orcid.org/0000-0002-4834-7607)
Institutions
- Jiangnan University (CN)
- Changzhou University (CN)
Publication Details
- Journal
- PeerJ
- Published
- 2025-08-05
- DOI
- https://doi.org/10.7717/peerj.19637
- Primary Topic
- Metabolomics and Mass Spectrometry Studies
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Natural Science Foundation of China
- Natural Science Foundation of Jiangsu Province