Imperial College London
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DrLeonidChindelevitch

Faculty of Medicine, School of Public Health

Lecturer in Infectious Disease Epidemiology
 
 
 
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Contact

 

l.chindelevitch Website

 
 
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Location

 

Sir Michael Uren HubWhite City Campus

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Summary

 

Publications

Citation

BibTex format

@article{Gan:2020:10.1101/2020.09.17.301226,
author = {Gan, GL and Nguyen, MH and Willie, E and Rezaie, MH and Lee, B and Chauve, C and Libbrecht, M and Chindelevitch, L},
doi = {10.1101/2020.09.17.301226},
title = {Geographic heterogeneity impacts drug resistance predictions in <i>Mycobacterium tuberculosis</i>},
url = {http://dx.doi.org/10.1101/2020.09.17.301226},
year = {2020}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - <jats:title>Abstract</jats:title><jats:p>The efficacy of antibiotic drug treatments in tuberculosis (TB) is significantly threatened by the development of drug resistance. There is a need for a robust diagnostic system that can accurately predict drug resistance in patients. In recent years, researchers have been taking advantage of whole-genome sequencing (WGS) data to infer antibiotic resistance. In this work we investigate the power of machine learning tools in inferring drug resistance from WGS data on three distinct datasets differing in their geographical diversity.</jats:p><jats:p>We analyzed data from the Relational Sequencing TB Data Platform, which comprises global isolates from 32 different countries, the PATRIC database, containing isolates contributed by researchers around the world, and isolates collected by the British Columbia Centre for Disease Control in Canada. We predicted drug resistance to the first-line drugs: isoniazid, rifampicin, ethambutol, pyrazinamide, and streptomycin. We focused on the genes which previous evidence suggests are involved in drug resistance in TB.</jats:p><jats:p>We called single-nucleotide polymorphisms using the Snippy pipeline, then applied different machine learning models. Following best practices, we chose the best parameters for each model via cross-validation on the training set and evaluated the performance via the sensitivity-specificity tradeoffs on the testing set.</jats:p><jats:p>To the best of our knowledge, our study is the first to predict antibiotic resistance in TB across multiple datasets. We obtained a performance comparable to that seen in previous studies, but observed that performance may be negatively affected when training on one dataset and testing on another, suggesting the importance of geographical heterogeneity in drug resistance predictions. In addition, we investigated the importance of each gene within each model, and recapitulated som
AU  - Gan,GL
AU  - Nguyen,MH
AU  - Willie,E
AU  - Rezaie,MH
AU  - Lee,B
AU  - Chauve,C
AU  - Libbrecht,M
AU  - Chindelevitch,L
DO  - 10.1101/2020.09.17.301226
PY  - 2020///
TI  - Geographic heterogeneity impacts drug resistance predictions in <i>Mycobacterium tuberculosis</i>
UR  - http://dx.doi.org/10.1101/2020.09.17.301226
ER  -
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