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

ABSTRACT: 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 be

SUBMITTER: Jin C 

PROVIDER: S-EPMC10809114 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature