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Sequence homology score-based deep fuzzy network for identifying therapeutic peptides

Authors :
Guo, Xiaoyi
Zheng, Ziyu
Cheong, Kang Hao
Zou, Quan
Tiwari, Prayag
Ding, Yijie
Guo, Xiaoyi
Zheng, Ziyu
Cheong, Kang Hao
Zou, Quan
Tiwari, Prayag
Ding, Yijie
Publication Year :
2024

Abstract

The detection of therapeutic peptides is a topic of immense interest in the biomedical field. Conventional biochemical experiment-based detection techniques are tedious and time-consuming. Computational biology has become a useful tool for improving the detection efficiency of therapeutic peptides. Most computational methods do not consider the deviation caused by noise. To improve the generalization performance of therapeutic peptide prediction methods, this work presents a sequence homology score-based deep fuzzy echo-state network with maximizing mixture correntropy (SHS-DFESN-MMC) model. Our method is compared with the existing methods on eight types of therapeutic peptide datasets. The model parameters are determined by 10 fold cross-validation on their training sets and verified by independent test sets. Across the 8 datasets, the average area under the receiver operating characteristic curve (AUC) values of SHS-DFESN-MMC are the highest on both the training (0.926) and independent sets (0.923). © 2024 The Authors

Details

Database :
OAIster
Notes :
English
Publication Type :
Electronic Resource
Accession number :
edsoai.on1457592905
Document Type :
Electronic Resource
Full Text :
https://doi.org/10.1016.j.neunet.2024.106458