EAVPFunc：融合蛋白質語言模型的集成框架用於識別抗病毒肽及其功能分類;EAVPFunc: An Ensemble-Based Framework with the Protein Language Model for Antiviral Peptides Identification and Functional Classification

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題名:	EAVPFunc：融合蛋白質語言模型的集成框架用於識別抗病毒肽及其功能分類;EAVPFunc: An Ensemble-Based Framework with the Protein Language Model for Antiviral Peptides Identification and Functional Classification
作者:	鄭雅嶸;Jeng, Ya-Rong
貢獻者:	資訊工程學系
關鍵詞:	抗病毒肽;整合模型;深度學習和機器學習;蛋白質語言模型;Antiviral peptides;ensemble model;deep learning and machine learning;protein language models
日期:	2024-01-27
上傳時間:	2024-09-19 17:14:37 (UTC+8)
出版者:	國立中央大學
摘要:	在21世紀，病毒性疾病的爆發對人類社會造成了重大影響。抗病毒肽（AVPs）作為對抗新興病毒疾病如SARS-CoV-2以及 HIV 和 HCV 等抗藥性菌株的重要治療藥物。然而，對於抗病毒肽的功能分類研究有限，以及不同病毒家族和物種之間的數據不均衡，對該領域構成了挑戰。為了克服這些挑戰，本研究引入了一個名為EAVPFunc的新型雙階段分類模型，旨在揭示抗病毒肽的功能特性。在第一階段，EAVPFunc將抗病毒肽從廣泛的肽譜中區分出來，將其與非抗微生物和非抗病毒的抗微生物肽區分。第二階段，EAVPFunc將抗病毒肽與特定病毒科和個別病毒進行精確對應。EAVPFunc結合了隨機森林演算法和卷積神經網絡，在一個集成模型中使用手工特徵和先進的蛋白質語言模型來提高解釋性和預測準確性。這種方法在兩個不同的數據集上達到了94.35%和99.46%的高準確率，超越了現有分類器在準確性和均衡分類任務上的表現。總之，我們提出EAVPFunc作為一個穩定且均衡的分類框架，代表了生物信息學方面的重大進步。;Viral outbreaks have had a significant impact on human society in the 21st century. Antiviral peptides (AVPs) are crucial therapeutic agents in the fight against emerging viral diseases such as SARS-CoV-2 and drug-resistant strains such as HIV and HCV. However, the limited research in functional classification poses a challenge for the field, along with data imbalance across different viral families and species. To overcome these challenges, the research introduces a new two-stage classification model named EAVPFunc, which aims to reveal the functional properties of AVPs. In the first stage, AVPs are distinguished from a wider range of peptides, including non-antimicrobial and non-antiviral peptides. In the second stage, EAVPFunc associates AVPs with specific virus families and individual viruses. EAVPFunc combines the Random Forest algorithm with Convolutional Neural Networks in an ensemble model, using handcrafted features and an advanced protein language model to improve interpretability and prediction accuracy. This approach resulted in high accuracy rates of 94.35% and 99.46% on two different datasets, outperforming existing classifiers in accuracy and balanced classification tasks. In conclusion, we propose EAVPFunc as a stable and balanced classification framework, which represents a significant advancement in bioinformatics.
顯示於類別:	[資訊工程研究所] 博碩士論文

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