Genome-Wide Mutation Scoring for Machine-Learning-Based Antimicrobial Resistance Prediction.

ABSTRACT: The prediction of antimicrobial resistance (AMR) based on genomic information can improve patient outcomes. Genetic mechanisms have been shown to explain AMR with accuracies in line with standard microbiology laboratory testing. To translate genetic mechanisms into phenotypic AMR, machine learning has been successfully applied. AMR machine learning models typically use nucleotide k-mer counts to represent genomic sequences. While k-mer representation efficiently captures sequence variation, it also results in high-dimensional and sparse data. With limited training data available, achieving acceptable model performance or model interpretability is challenging. In this study, we explore the utility of feature engineering with several biologically relevant signals. We propose to predict the f

SUBMITTER: Majek P 

PROVIDER: S-EPMC8657983 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature