Interpreting <i>k</i>-mer–based signatures for antibiotic resistance prediction

Abstract

By enhancing the interpretability of genomic k-mer-based antibiotic resistance prediction models, our approach improves their clinical utility and hence will facilitate their adoption in routine diagnostics by clinicians and microbiologists. While antibiotic resistance was the motivating application, the method is generic and can be transposed to any other bacterial trait. An R package implementing our method is available at https://gitlab.com/biomerieux-data-science/clustlasso.