iMRSA-Fuse: A Fast and Accurate Computational Approach for Predicting Anti-MRSA Peptides by Fusing Multi-View Information
1
Issued Date
2025-01-01
Resource Type
eISSN
29984165
Scopus ID
2-s2.0-105016304883
Journal Title
IEEE Transactions on Computational Biology and Bioinformatics
Volume
22
Issue
1
Start Page
2
End Page
12
Rights Holder(s)
SCOPUS
Bibliographic Citation
IEEE Transactions on Computational Biology and Bioinformatics Vol.22 No.1 (2025) , 2-12
Suggested Citation
Charoenkwan P., Schaduangrat N., Moni M.A., Shoombuatong W. iMRSA-Fuse: A Fast and Accurate Computational Approach for Predicting Anti-MRSA Peptides by Fusing Multi-View Information. IEEE Transactions on Computational Biology and Bioinformatics Vol.22 No.1 (2025) , 2-12. 12. doi:10.1109/TCBBIO.2024.3496503 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/112291
Title
iMRSA-Fuse: A Fast and Accurate Computational Approach for Predicting Anti-MRSA Peptides by Fusing Multi-View Information
Author's Affiliation
Corresponding Author(s)
Other Contributor(s)
Abstract
Methicillin-resistant S. aureus (MRSA) has prominently emerged among the recognized causes of community-acquired and hospital infections. We proposed a novel computational approach, iMRSA-Fuse, based on a multi-view feature fusion strategy for fast and accurate anti-MRSA peptide identification. In iMRSA-Fuse, we explored and integrated 12 different sequence-based feature descriptors from multiple perspectives, in conjunction with 12 popular machine learning (ML) algorithms, to construct multi-view features that were able to fully capture the useful information of anti-MRSA peptides. Additionally, we applied our customized genetic algorithm to determine a set of multi-view features to enhance its discriminative ability. Based on a series of comparative results, our multi-view features exhibited the most discriminative ability compared to several conventional feature descriptors. Moreover, concerning the independent test dataset, iMRSA-Fuse achieved the best balanced accuracy (BACC) and Matthew's correlation coefficient (MCC) of 0.997 and 0.981, respectively with an increase of 3.93 and 7.78%, respectively. Finally, to facilitate the large-scale identification of candidate anti-MRSA peptides, a user-friendly web server of the iMRSA-Fuse model is constructed and is freely accessible at https://pmlabqsar.pythonanywhere.com/iMRSA-Fuse. We anticipate that this new computational approach will be effectively applied to screen and prioritize candidate peptides that might exhibit the great anti-MRSA activities.