ArticleBMC bioinformatics2018
Predicting bacterial resistance from whole-genome sequences using k-mers and stability selection.
Article in BMC bioinformatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed.
- BacT-Seq, a Nanopore-Based Whole-Genome Sequencing Workflow Prototype for Rapid and Accurate Pathogen Identification and Resistance Prediction from Positive Blood Cultures: A Feasibility Study.Diagnostics (Basel, Switzerland) · 2026Article
- Prediction of antimicrobial resistance inMicrobiology spectrum · 2025Article
- A survey of k-mer methods and applications in bioinformatics.Computational and structural biotechnology journal · 2024Review
- Using genomic data and machine learning to predict antibiotic resistance: A tutorial paper.PLoS computational biology · 2024Article
- Data-Driven Approaches in Antimicrobial Resistance: Machine Learning Solutions.Antibiotics (Basel, Switzerland) · 2024Article
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- metaProbiotics: a tool for mining probiotic from metagenomic binning data based on a language model.Briefings in bioinformatics · 2024Article
- PredictingMicroorganisms · 2024Article
- From genotype to phenotype: computational approaches for inferring microbial traits relevant to the food industry.FEMS microbiology reviews · 2023Review
- Article
- Geographical classification of malaria parasites through applying machine learning to whole genome sequence data.Scientific reports · 2022Article
- Antimicrobial Properties of Plant Fibers.Molecules (Basel, Switzerland) · 2022Review
- Antimicrobial Resistance and Its Spread Is a Global Threat.Antibiotics (Basel, Switzerland) · 2022Review
- SeqScreen: accurate and sensitive functional screening of pathogenic sequences via ensemble learning.Genome biology · 2022Article
- Overview of bioinformatic methods for analysis of antibiotic resistome from genome and metagenome data.Journal of microbiology (Seoul, Korea) · 2021Review
- Machine learning predicts and provides insights into milk acidification rates of Lactococcus lactis.PloS one · 2021Article
- Robust detection of point mutations involved in multidrug-resistant Mycobacterium tuberculosis in the presence of co-occurrent resistance markers.PLoS computational biology · 2020Article
- Article
- Article
- Predicting Phenotypic Polymyxin Resistance in Klebsiella pneumoniae through Machine Learning Analysis of Genomic Data.mSystems · 2020Article
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Authors and funding
2 authors.
Funding
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Abstract
backgroundSeveral 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, 2010), that consists in penalizing logistic regression models with a Lasso penalty, coupled with extensive resampling procedures.
resultsUsing public datasets, we applied the resulting classifiers to two bacterial species and achieved predictive performance equivalent to state of the art. The models are extremely sparse, involving 1 to 8 k-mers per antibiotic, hence are remarkably easy and fast to evaluate on new genomes (from raw reads to assemblies).
conclusionOur proof of concept therefore demonstrates that stability selection is a powerful approach to investigate bacterial genotype-phenotype relationships.
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