1. New Multi-View Feature Learning Method for Accurate Antifungal Peptide Detection.
- Author
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Ferdous, Sayeda Muntaha, Mugdha, Shafayat Bin Shabbir, and Dehzangi, Iman
- Subjects
PEPTIDES ,MYCOSES ,AMINO acids ,DRUG resistance in microorganisms ,STATISTICAL correlation ,COMPUTATIONAL neuroscience - Abstract
Antimicrobial resistance, particularly the emergence of resistant strains in fungal pathogens, has become a pressing global health concern. Antifungal peptides (AFPs) have shown great potential as a promising alternative therapeutic strategy due to their inherent antimicrobial properties and potential application in combating fungal infections. However, the identification of antifungal peptides using experimental approaches is time-consuming and costly. Hence, there is a demand to propose fast and accurate computational approaches to identifying AFPs. This paper introduces a novel multi-view feature learning (MVFL) model, called AFP-MVFL, for accurate AFP identification, utilizing multi-view feature learning. By integrating the sequential and physicochemical properties of amino acids and employing a multi-view approach, the AFP-MVFL model significantly enhances prediction accuracy. It achieves 97.9%, 98.4%, 0.98, and 0.96 in terms of accuracy, precision, F1 score, and Matthews correlation coefficient (MCC), respectively, outperforming previous studies found in the literature. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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