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- Title
Multi-Label Random Forest Model for Tuberculosis Drug Resistance Classification and Mutation Ranking.
- Authors
Kouchaki, Samaneh; Yang, Yang; Lachapelle, Alexander; Walker, Timothy M.; Walker, A. Sarah; Peto, Timothy E. A.; Crook, Derrick W.; Clifton, David A.
- Abstract
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 Mycobacterium tuberculosis 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.
- Subjects
DRUG resistance; TUBERCULOSIS; MYCOBACTERIUM tuberculosis; FORECASTING; LABELS; SOURCE code; RIFAMPIN
- Publication
Frontiers in Microbiology, 2020, Vol 11, p1
- ISSN
1664-302X
- Publication type
Article
- DOI
10.3389/fmicb.2020.00667