计算机科学
人工智能
抗菌肽
杠杆(统计)
深度学习
机器学习
嵌入
卷积神经网络
抗菌剂
生物
微生物学
作者
Daniel Veltri,Uday Kamath,Amarda Shehu
出处
期刊:Bioinformatics
[Oxford University Press]
日期:2018-03-24
卷期号:34 (16): 2740-2747
被引量:260
标识
DOI:10.1093/bioinformatics/bty179
摘要
Abstract Motivation Bacterial resistance to antibiotics is a growing concern. Antimicrobial peptides (AMPs), natural components of innate immunity, are popular targets for developing new drugs. Machine learning methods are now commonly adopted by wet-laboratory researchers to screen for promising candidates. Results In this work, we utilize deep learning to recognize antimicrobial activity. We propose a neural network model with convolutional and recurrent layers that leverage primary sequence composition. Results show that the proposed model outperforms state-of-the-art classification models on a comprehensive dataset. By utilizing the embedding weights, we also present a reduced-alphabet representation and show that reasonable AMP recognition can be maintained using nine amino acid types. Availability and implementation Models and datasets are made freely available through the Antimicrobial Peptide Scanner vr.2 web server at www.ampscanner.com. Supplementary information Supplementary data are available at Bioinformatics online.
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