Enhancing Protein Sequence Classification with a Fuzzy Neural Network: A Study in Anticancer Peptide Identification
學年 113
學期 1
發表日期 2024-08-10
作品名稱 Enhancing Protein Sequence Classification with a Fuzzy Neural Network: A Study in Anticancer Peptide Identification
作品名稱（其他語言）
著者 Khanh Le; Nguyen Quoc and Nguyen; Van-Nui and Nguyen, Thi-Tuyen and Tran; Thi-Xuan and Ho; Trang-Thi
作品所屬單位
出版者
會議名稱 2024 International Conference on Fuzzy Theory and Its Applications (iFUZZY)
會議地點 Kagawa, Japan
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.
關鍵字 Fuzzy Neural Networks , Protein Sequence Classification , Anticancer Peptides , Bioinformatics , Genetic Algorithms , Feature Selection
語言 en
收錄於
會議性質 國內
校內研討會地點 無
研討會時間 20240810~20240813
通訊作者
國別 JPN
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機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/126233 )