DeepBP: Ensemble deep learning strategy for bioactive peptide prediction
In this study, CapsuleGAN, GRU, and CNN were successfully employed as base classifiers to implement ensemble learning, which not only achieved good results in the prediction of two datasets but also surpassed existing methods. The ability to predict peptides with strong ACE inhibitory activity and ACPs more accurately and quickly is significant, and this work provides valuable insights for predicting other functional peptides. The source code and dataset for this experiment are publicly available at https://github.com/Zhou-Jianren/bioactive-peptides .
Authors
- Fang Ge (ORCID: https://orcid.org/0000-0001-5792-5379)
- Xiaohua Wang (ORCID: https://orcid.org/0000-0002-6410-2319)
- Jianling Zhou (ORCID: https://orcid.org/0000-0001-7599-2612)
- Xun Wang (ORCID: https://orcid.org/0000-0001-5566-4689)
- Ming Zhang (ORCID: https://orcid.org/0000-0002-6217-2882)
Institutions
- Nanjing University of Posts and Telecommunications (CN)
- Jiangsu University of Science and Technology (CN)
Publication Details
- Journal
- BMC Bioinformatics
- Published
- 2024-11-11
- DOI
- https://doi.org/10.1186/s12859-024-05974-5
- Citations
- 14
- Primary Topic
- Machine Learning in Bioinformatics
- Type
- article
- Field-Weighted Citation Impact
- 3.27
Funders
- Nanjing University of Posts and Telecommunications
- Nanjing University