轻工学报2024，Vol.39Issue(2)：12-18,7.DOI:10.12187/2024.02.002
基于深度学习和蛋白质语言模型的抗菌肽预测模型研究
Research on antimicrobial peptide prediction model based on deep learning and protein language model
摘要
Abstract
In response to the need for improving prediction accuracy(ACC)in existing models for Antimicrobial Peptides(AMPs),a novel AMP prediction model called DeepGlap was proposed.This model utilized two protein language models for feature extraction from AMP sequences,followed by fusion of feature vectors.These fused vectors were then input into a deep learning network composed of multiple layers of bidirectional long short-term memory networks(mBi-LSTM),one-dimensional convolutional neural networks(1D-CNN),and attention mechanisms.The model underwent performance evaluation and optimization.Results indicated that the model achieved ACC,the Pearson correlation coefficient(MCC),and the area urder the curve(AUC)values of 0.739,0.489,and 0.81,respectively,demonstrating superior predictive performance compared to existing AMP prediction models.关键词
抗菌肽/预测模型/食源性病原体/蛋白质语言模型/深度学习网络Key words
antimicrobial peptide/prediction model/foodborne pathogen/protein language model/deep learning network分类
轻工纺织引用本文复制引用
王晓,吴洲,王宏伟,王榕,陈浩然..基于深度学习和蛋白质语言模型的抗菌肽预测模型研究[J].轻工学报,2024,39(2):12-18,7.基金项目
国家自然科学基金青年科学基金项目(32101976,61906175) （32101976,61906175）
河南省科技攻关项目(232102210020,20210221014) （232102210020,20210221014）
河南省高等学校重点科研项目(22A520013,23B520004) （22A520013,23B520004）
