Predicting bacterial resistance from whole-genome sequences using k-mers and stability selection.

ABSTRACT:

Background

Several studies demonstrated the feasibility of predicting bacterial antibiotic resistance phenotypes from whole-genome sequences, the prediction process usually amounting to detecting the presence of genes involved in antibiotic resistance mechanisms, or of specific mutations, previously identified from a training panel of strains, within these genes. We address the problem from the supervised statistical learning perspective, not relying on prior information about such resistance factors. We rely on a k-mer based genotyping scheme and a logistic regression model, thereby combining several k-mers into a probabilistic model. To identify a small yet predictive set of k-mers, we rely on the stability selection approach (Meinshausen et al., J R Stat Soc Ser B 72:417-73, 201

SUBMITTER: Mahe P 

PROVIDER: S-EPMC6192184 | biostudies-literature | 2018 Oct

REPOSITORIES: biostudies-literature