Article

Fighting antimicrobial resistance in <i>Pseudomonas aeruginosa</i> with machine learning-enabled molecular diagnostics

2019-05-24

Abstract excerpt

The growing importance of antibiotic resistance on clinical outcomes and cost of care underscores the need for optimization of current diagnostics. For a number of bacterial species antimicrobial resistance can be unambiguously predicted based on their genome sequence. In this study, we sequenced the genomes and transcriptomes of 414 drug-resistant clinical Pseudomonas aeruginosa isolates. By training machine lea...

Topics

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

Identifiers and source

Literature Corpus work: 1f67ea68-eec7-574e-b2c1-0ad980887239
DOI: 10.1101/643676

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Fighting antimicrobial resistance in <i>Pseudomonas aeruginosa</i> with machine learning-enabled molecular diagnosticsDOI 10.1101/643676Read

Minimal Gene Signatures Enable High-Accuracy Prediction of Antibiotic Resistance in <i>Pseudomonas aeruginosa</i>Semantic proximity Read

Keeping up with the pathogens: Improved antimicrobial resistance detection and prediction from<i>Pseudomonas</i>aeruginosa genomesSemantic proximity Read

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

Keeping up with the pathogens: improved antimicrobial resistance detection and prediction from Pseudomonas aeruginosa genomes.Semantic proximity Read

Direct prediction of antimicrobial resistance in Pseudomonas aeruginosa by metagenomic next-generation sequencingSemantic proximity Read

Precise prediction of antibiotic resistance in <i>Escherichia coli</i> from full genome sequencesSemantic proximity Read

An Automated Machine Learning Framework for Antimicrobial Resistance Prediction Through TranscriptomicsSemantic proximity Read

Genomic and phenotypic inconsistencies in Pseudomonas aeruginosa resistome among intensive care patients.Semantic proximity Read

Improved prediction of antimicrobial resistance in <i>Klebsiella pneumoniae</i> using machine learningSemantic proximity Read

Decoding Host-Pathogen Interactions in <i>Staphylococcus aureus</i> : Insights into Allelic Variation and Antimicrobial Resistance Prediction Using Artificial Intelligence and Machine Learning based approachesSemantic proximity Read

Predicting Pseudomonas aeruginosa susceptibility phenotypes from whole genome sequence resistome analysis.Semantic proximity Read

Correlation between phenotypic antibiotic susceptibility and the resistome in Pseudomonas aeruginosa.Semantic proximity Read