Multi-Label Random Forest Model for Tuberculosis Drug Resistance Classification and Mutation Ranking

Resistance prediction and mutation ranking are important tasks in the analysis of Tuberculosis sequence data. Due to standard regimens for the use of first-line antibiotics, resistance co-occurrence, in which samples are resistant to multiple drugs, is common. Analysing all drugs simultaneously shou...

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Vydané v:Frontiers in microbiology Ročník 11; s. 667
Hlavní autori: Kouchaki, Samaneh, Yang, Yang, Lachapelle, Alexander, Walker, Timothy M., Walker, A. Sarah, Peto, Timothy E. A., Crook, Derrick W., Clifton, David A.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Switzerland Frontiers Media S.A 22.04.2020
Predmet:
drug resistance
Microbiology
MLRF
mutation ranking
SLRF
tuberculosis
mutation ranking
SLRF
drug resistance
MLRF
tuberculosis
ISSN:1664-302X, 1664-302X
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Shrnutí:Resistance prediction and mutation ranking are important tasks in the analysis of Tuberculosis sequence data. Due to standard regimens for the use of first-line antibiotics, resistance co-occurrence, in which samples are resistant to multiple drugs, is common. Analysing all drugs simultaneously should therefore enable patterns reflecting resistance co-occurrence to be exploited for resistance prediction. Here, multi-label random forest (MLRF) models are compared with single-label random forest (SLRF) for both predicting phenotypic resistance from whole genome sequences and identifying important mutations for better prediction of four first-line drugs in a dataset of 13402 isolates. Results confirmed that MLRFs can improve performance compared to conventional clinical methods (by 18.10%) and SLRFs (by 0.91%). In addition, we identified a list of candidate mutations that are important for resistance prediction or that are related to resistance co-occurrence. Moreover, we found that retraining our analysis to a subset of top-ranked mutations was sufficient to achieve satisfactory performance. The source code can be found at http://www.robots.ox.ac.uk/~davidc/code.php.
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Reviewed by: Francesc Coll, University of London, United Kingdom; Wouter Deelder, University of London, United Kingdom
Edited by: Miguel Viveiros, New University of Lisbon, Portugal
This article was submitted to Antimicrobials, Resistance and Chemotherapy, a section of the journal Frontiers in Microbiology
ISSN:1664-302X
1664-302X
DOI:10.3389/fmicb.2020.00667