Antimicrobial Peptides Prediction Based on BERT and Ensemble Learning

被引:0
作者
Gao, Wanling [1 ]
Zhao, Jun [1 ]
Yue, Zhenyu [1 ]
机构
[1] School of Information and Artificial Intelligence, Anhui Agricultural University, Hefei
来源
Dianzi Keji Daxue Xuebao/Journal of the University of Electronic Science and Technology of China | 2024年 / 53卷 / 04期
关键词
antimicrobial peptides; assessment; BERT; ensemble learning; pre-trained model;
D O I
10.12178/1001-0548.2023295
中图分类号
学科分类号
摘要
As the best substitute for antibiotics, antimicrobial peptides (AMPs) have important research significance. How to accurately identify AMPs using computational methods has been a key issue in the field of bioinformatics in recent years. However, traditional machine learning methods require autonomous extraction and selection of features from sequence information, resulting in low AMPs identification accuracy. Faced with the above challenges, a deep learning prediction methods based on Bidirectional Encoder Representation from Transformers (BERT) is proposed. In order to conduct a comprehensive evaluation of existing BERT-based AMP tools and further improve the performance of AMP calculation methods, four existing BERT-based AMP prediction tools in terms of pre-training strategies, word vector embeddings, and prediction performance are compared, and thus a novel AMP prediction tool based on the idea of ensemble learning is proposed. The experimental results show that the proposed model has been improved on several performance evaluation indexes. © 2024 University of Electronic Science and Technology of China. All rights reserved.
引用
收藏
页码:635 / 640
页数:5
相关论文
共 27 条
[1]  
SMITH W P J, WUCHER B R, NADELL C D, Et al., Bacterial defences: Mechanisms, evolution and antimicrobial resistance, Nature Reviews Microbiology, 21, pp. 519-534, (2023)
[2]  
WANG G S, LI X, WANG Z., APD3: The antimicrobial peptide database as a tool for research and education, Nucleic Acids Research, 44, D1, pp. D1087-D1093, (2016)
[3]  
GAWDE U, CHAKRABORTY S, WAGHU F H, Et al., CAMPR4: A database of natural and synthetic antimicrobial peptides, Nucleic Acids Research, 51, D1, pp. D377-D383, (2023)
[4]  
KANG X Y, DONG F Y, SHI C, Et al., DRAMP 2.0, an updated data repository of antimicrobial peptides, Scientific Data, 6, (2019)
[5]  
JHONG J H, CHI Y H, LI W C, Et al., dbAMP: An integrated resource for exploring antimicrobial peptides with functional activities and physicochemical properties on transcriptome and proteome data, Nucleic Acids Research, 47, D1, pp. D285-D297, (2019)
[6]  
LIU M Y, LIU H M, ZHANG Z F, Et al., Review of machine learning prediction algorithms for antimicrobial peptides, Journal of University of Electronic Science and Technology of China, 51, 6, pp. 830-840, (2022)
[7]  
WAGHU F H, BARAI R S, GURUNG P, Et al., CAMPR3: A database on sequences, structures and signatures of antimicrobial peptides, Nucleic Acids Research, 44, D1, pp. D1094-D1097, (2016)
[8]  
SACHIN P, MADHU T, VIVEKANAND K, Et al., GEUAMP50: Enhanced antimicrobial peptide prediction using a machine learning approach, Materials Today: Proceedings, 73, pp. 81-87, (2023)
[9]  
BHADRA P, YAN J L, LI J Y, Et al., AmPEP: Sequence-based prediction of antimicrobial peptides using distribution patterns of amino acid properties and random forest, Scientific Reports, 8, (2018)
[10]  
LATA S, MISHRA N K, RAGHAVA G P., AntiBP2: Improved version of antibacterial peptide prediction, BMC Bioinformatics, 11, (2010)