Enhancing Protein Sequence Classification with a Fuzzy Neural Network: A Study in Anticancer Peptide Identification

doi:10.1109/iFUZZY63051.2024.10662887

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題名:	Enhancing Protein Sequence Classification with a Fuzzy Neural Network: A Study in Anticancer Peptide Identification
作者:	Le, Khanh;Nguyen, Nguyen Quoc and;Van-Nui and Nguyen, Thi-Tuyen and Tran;Ho, Thi-Xuan and;Trang-Thi
關鍵詞:	Fuzzy Neural Networks , Protein Sequence Classification , Anticancer Peptides , Bioinformatics , Genetic Algorithms , Feature Selection
日期:	2024-08-10
上傳時間:	2024-09-20 12:08:43 (UTC+8)
出版者:	IEEE
摘要:	In bioinformatics, classifying protein sequences into anticancer peptides (ACPs) and non-ACPs is crucial yet challenging due to the inherent uncertainties of biological data. This study introduces a novel fuzzy neural network (FNN) model that integrates fuzzy logic within neural network architectures, enhancing the handling of ambiguity and improving classification accuracy. Our model, tested against several conventional machine learning models and recent studies, demonstrated superior specificity (83.28%) and overall accuracy (79.14%), marking a significant advancement in the identification of therapeutically relevant peptides. The integration of fuzzy logic not only optimized the performance but also increased the interpretability of the results, making it a valuable tool for complex bioinformatic analyses. These findings underscore the potential of fuzzy systems to refine predictive capabilities in computational biology, aligning perfectly with the themes of enhancing fuzzy theory applications in practical and impactful ways.
DOI:	10.1109/iFUZZY63051.2024.10662887
顯示於類別:	[資訊工程學系暨研究所] 會議論文

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