Structured Sparse Regularized TSK Fuzzy System for predicting therapeutic peptides

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.

Automated summary

Therapeutic peptides have multiple benefits on human skeletal, digestive, and blood systems, including antibacterial properties and anti-inflammatory effects. In order to reduce resource consumption for wet experiments, computational-based methods have been developed for therapeutic peptide identification, and our proposed method, SSR-TSK-FS-WCS, shows promising performance on multiple therapeutic peptides and UCI datasets.