Structured Sparse Regularized TSK Fuzzy System for predicting therapeutic peptides. Issue 3 (18th April 2022)

• Record Type: Journal Article
• Authors: Guo, Xiaoyi; Jiang, Yizhang; Zou, Quan
• Is Part Of: Briefings in bioinformatics. Volume 23:Issue 3(2022)
• Publication Date: 2022-04-18
• Subjects: therapeutic peptides -- group sparse regularization -- protein sequence classification -- Takagi–Sugeno–Kang fuzzy system -- within-class scatter; Genetics -- Data processing -- Periodicals; Molecular biology -- Data processing -- Periodicals; Genomes -- Data processing -- Periodicals; 572.80285
• DOI: 10.1093/bib/bbac135
• Languages: English
• ISSNs: 1467-5463

Abstract

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.