Developing an in silico minimum inhibitory concentration panel test for Klebsiella pneumoniae View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2018-01-11

AUTHORS

Marcus Nguyen, Thomas Brettin, S. Wesley Long, James M. Musser, Randall J. Olsen, Robert Olson, Maulik Shukla, Rick L. Stevens, Fangfang Xia, Hyunseung Yoo, James J. Davis

ABSTRACT

Antimicrobial resistant infections are a serious public health threat worldwide. Whole genome sequencing approaches to rapidly identify pathogens and predict antibiotic resistance phenotypes are becoming more feasible and may offer a way to reduce clinical test turnaround times compared to conventional culture-based methods, and in turn, improve patient outcomes. In this study, we use whole genome sequence data from 1668 clinical isolates of Klebsiella pneumoniae to develop a XGBoost-based machine learning model that accurately predicts minimum inhibitory concentrations (MICs) for 20 antibiotics. The overall accuracy of the model, within ±1 two-fold dilution factor, is 92%. Individual accuracies are ≥90% for 15/20 antibiotics. We show that the MICs predicted by the model correlate with known antimicrobial resistance genes. Importantly, the genome-wide approach described in this study offers a way to predict MICs for isolates without knowledge of the underlying gene content. This study shows that machine learning can be used to build a complete in silico MIC prediction panel for K. pneumoniae and provides a framework for building MIC prediction models for other pathogenic bacteria. More... »

PAGES

421

Identifiers

URI

http://scigraph.springernature.com/pub.10.1038/s41598-017-18972-w

DOI

http://dx.doi.org/10.1038/s41598-017-18972-w

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1100212536

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/29323230


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90 public health threat
91 resistance genes
92 resistance phenotype
93 resistant infections
94 sequence data
95 sequencing approach
96 serious public health threat
97 silico MIC prediction panel
98 silico minimum inhibitory concentration panel test
99 study
100 test
101 test turnaround time
102 threat
103 time
104 turn
105 turnaround time
106 two-fold dilution factor
107 way
108 whole genome sequencing approach
109 whole-genome sequence data
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