Apr 29, 2020

Predicting Antimicrobial Resistance Using Conserved Genes

BioRxiv : the Preprint Server for Biology
M. NguyenJames J Davis

Abstract

A growing number of studies have shown that machine learning algorithms can be used to accurately predict antimicrobial resistance (AMR) phenotypes from bacterial sequence data. In these studies, models are typically trained using input features derived from comprehensive sets of known AMR genes or whole genome sequences. However, it can be difficult to determine whether genomes and their corresponding sets of AMR genes are complete when sequencing contaminated or metagenomic samples. In this study, we explore the possibility of using incomplete genome sequence data to predict AMR phenotypes. Machine learning models were built from randomly-selected sets of core genes that are held in common among the members of a species, and the AMR-conferring genes were removed based on their protein annotations. For Klebsiella pneumoniae, Mycobacterium tuberculosis, Salmonella enterica, and Staphylococcus aureus, we report that it is possible to classify susceptible and resistant phenotypes with average F1 scores ranging from 0.80-0.89 with as few as 100 conserved non-AMR genes, with very major error rates ranging from 0.11-0.23 and major error rates ranging from 0.10-0.20. Models built from core genes have predictive power in the cases whe...Continue Reading

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Mentioned in this Paper

Human Connectome
Structure of Cortex of Kidney
Human Connectome Project
Structure
Cellular Component Organization
Cerebral Cortex
Adrenal Cortex
Spatial Projection
EAF2 gene
Encephalitis Lethargica

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