Structured Sparse Regularized TSK Fuzzy System for predicting therapeutic peptides

Abstract Therapeutic peptides act on the skeletal system, digestive system and blood system, have antibacterial properties and help relieve inflammation. In order to reduce the resource consumption of wet experiments for the identification of therapeutic peptides, many computational-based methods ha...

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Bibliographic Details
Published in:Briefings in bioinformatics 2022-05, Vol.23 (3)
Main Authors: Guo, Xiaoyi, Jiang, Yizhang, Zou, Quan
Format: Article
Language:English
Subjects:
Algorithms
Computer applications
Digestive system
Fuzzy Logic
Identification methods
Machine Learning
Peptides
Peptides - therapeutic use
Resource consumption
Skeletal system
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Summary:Abstract Therapeutic peptides act on the skeletal system, digestive system and blood system, have antibacterial properties and help relieve inflammation. In order to reduce the resource consumption of wet experiments for the identification of therapeutic peptides, many computational-based methods have been developed to solve the identification of therapeutic peptides. Due to the insufficiency of traditional machine learning methods in dealing with feature noise. We propose a novel therapeutic peptide identification method called Structured Sparse Regularized Takagi–Sugeno–Kang Fuzzy System on Within-Class Scatter (SSR-TSK-FS-WCS). Our method achieves good performance on multiple therapeutic peptides and UCI datasets.
ISSN:1467-5463
1477-4054
DOI:10.1093/bib/bbac135