Predicting antimicrobial resistance in E. coli with discriminative position fused deep learning classifier.

Autor:	Jin C; School of Computer and Computing Science, Hangzhou City University, Hangzhou 310015, China., Jia C; Institute of Preventive Veterinary Sciences and Department of Veterinary Medicine, Zhejiang University College of Animal Sciences, Hangzhou 310058, China., Hu W; School of Computer and Computing Science, Hangzhou City University, Hangzhou 310015, China.; College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China., Xu H; School of Computer and Computing Science, Hangzhou City University, Hangzhou 310015, China., Shen Y; School of Computer and Computing Science, Hangzhou City University, Hangzhou 310015, China., Yue M; Institute of Preventive Veterinary Sciences and Department of Veterinary Medicine, Zhejiang University College of Animal Sciences, Hangzhou 310058, China.; Hainan Institute of Zhejiang University, Sanya 572000, China.; Zhejiang Provincial Key Laboratory of Preventive Veterinary Medicine, Hangzhou 310058, China.; State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, National Medical Center for Infectious Diseases, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou 310003, China.
Jazyk:	angličtina
Zdroj:	Computational and structural biotechnology journal [Comput Struct Biotechnol J] 2023 Dec 29; Vol. 23, pp. 559-565. Date of Electronic Publication: 2023 Dec 29 (Print Publication: 2024).
DOI:	10.1016/j.csbj.2023.12.041
Abstrakt:	Escherichia coli ( E. coli ) has become a particular concern due to the increasing incidence of antimicrobial resistance (AMR) observed worldwide. Using machine learning (ML) to predict E. coli AMR is a more efficient method than traditional laboratory testing. However, further improvement in the predictive performance of existing models remains challenging. In this study, we collected 1937 high-quality whole genome sequencing (WGS) data from public databases with an antimicrobial resistance phenotype and modified the existing workflow by adding an attention mechanism to enable the modified workflow to focus more on core single nucleotide polymorphisms (SNPs) that may significantly lead to the development of AMR in E. coli. While comparing the model performance before and after adding the attention mechanism, we also performed a cross-comparison among the published models using random forest (RF), support vector machine (SVM), logistic regression (LR), and convolutional neural network (CNN). Our study demonstrates that the discriminative positional colors of Chaos Game Representation (CGR) images can selectively influence and highlight genome regions without prior knowledge, enhancing prediction accuracy. Furthermore, we developed an online tool (https://github.com/tjiaa/E.coli-ML/tree/main) for assisting clinicians in the rapid prediction of the AMR phenotype of E. coli and accelerating clinical decision-making. Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. (© 2024 The Authors.)
Databáze:	MEDLINE
Externí odkaz:	Zobrazit plný text záznamu