Article

Evaluation of parameters affecting performance and reliability of machine learning-based antibiotic susceptibility testing from whole genome sequencing data

2019-04-12

Abstract excerpt

Prediction of antibiotic resistance phenotypes from whole genome sequencing data by machine learning methods has been proposed as a promising platform for the development of sequence-based diagnostics. However, there has been no systematic evaluation of factors that may influence performance of such models, how they might apply to and vary across clinical populations, and what the implications might be in the clin...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: 318ed558-525c-5a8a-8e5f-91b22d52322d
DOI: 10.1101/607127

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Evaluation of parameters affecting performance and reliability of machine learning-based antibiotic susceptibility testing from whole genome sequencing dataDOI 10.1101/607127Read

A machine learning framework to predict antibiotic resistance traits and yet unknown genes underlying resistance to specific antibiotics in bacterial strains.Semantic proximity Read

Machine Learning Predicts Antimicrobial Resistance from Genomic Data across ESKAPEE PathogensSemantic proximity Read

Precise prediction of antibiotic resistance in Escherichia coli from full genome sequencesSemantic proximity Read

Optimising machine learning prediction of minimum inhibitory concentrations in Klebsiella pneumoniaeSemantic proximity Read

Towards routine employment of computational tools for antimicrobial resistance determination via high-throughput sequencingSemantic proximity Read

Machine learning to predict ceftriaxone resistance using single nucleotide polymorphisms within a global database of Neisseria gonorrhoeae genomes.Semantic proximity Read

Prediction of Antibiotic Resistance Phenotypes and Minimum Inhibitory Concentrations in Salmonella Using Machine Learning Analysis of Its Pan-Genome and Pan-Resistome Features.Semantic proximity Read

Accessory genes define species-specific pathways to antibiotic resistanceSemantic proximity Read

Confidence-based Prediction of Antibiotic Resistance at the Patient LevelSemantic proximity Read

Machine learning prediction of resistance to sub-inhibitory antimicrobial concentrations from Escherichia coli genomesSemantic proximity Read

Integrating Phenotypic and Genomic Data with Machine Learning to Predict Antimicrobial Resistance and Identify Genetic Biomarkers in E. coliSemantic proximity Read

A systematic analysis of machine learning pipelines for robust antimicrobial resistance predictionSemantic proximity Read