會議論文
| 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 |
| 公開徵稿 | |
| 出版型式 | |
| 出處 | |
| 相關連結 |
機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/126233 ) |