Kouchaki, S;
Yang, Y;
Lapachelle, A;
Walker, TM;
Walker, A;
CRyPTIC Consortium;
Peto, T;
... Clifton, DA; + view all
(2020)
Multi-Label Random Forest Model for Tuberculosis Drug Resistance Classification and Mutation Ranking.
Frontiers in Microbiology
10.3389/fmicb.2020.00667.
(In press).
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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.
| Type: | Article |
|---|---|
| Title: | Multi-Label Random Forest Model for Tuberculosis Drug Resistance Classification and Mutation Ranking |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.3389/fmicb.2020.00667 |
| Publisher version: | https://www.doi.org/10.3389/fmicb.2020.00667 |
| Language: | English |
| Additional information: | © 2020 Kouchaki, Yang, Lachapelle, Walker, Walker, Peto, Crook and Clifton. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Inst of Clinical Trials and Methodology UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Inst of Clinical Trials and Methodology > MRC Clinical Trials Unit at UCL |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10094072 |
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