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 |
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| Hlavní autori: | , , , , , , , |
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Frontiers Media S.A
22.04.2020
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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
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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| AbstractList | 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. 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. 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.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. |
| Author | Kouchaki, Samaneh Walker, Timothy M. Lachapelle, Alexander Walker, A. Sarah Clifton, David A. Peto, Timothy E. A. Yang, Yang Crook, Derrick W. |
| AuthorAffiliation | 3 Nuffield Department of Medicine, University of Oxford , Oxford , United Kingdom 6 NIHR Biomedical Research Centre , Oxford , United Kingdom 2 Oxford-Suzhou Centre for Advanced Research , Suzhou , China 5 Oxford University Clinical Research Unit , Ho Chi Minh City , Vietnam 1 Department of Engineering Science, Institute of Biomedical Engineering, University of Oxford , Oxford , United Kingdom 4 National Institute of Health Research Oxford Biomedical Research Centre, John Radcliffe Hospital , Oxford , United Kingdom |
| AuthorAffiliation_xml | – name: 1 Department of Engineering Science, Institute of Biomedical Engineering, University of Oxford , Oxford , United Kingdom – name: 2 Oxford-Suzhou Centre for Advanced Research , Suzhou , China – name: 6 NIHR Biomedical Research Centre , Oxford , United Kingdom – name: 3 Nuffield Department of Medicine, University of Oxford , Oxford , United Kingdom – name: 5 Oxford University Clinical Research Unit , Ho Chi Minh City , Vietnam – name: 4 National Institute of Health Research Oxford Biomedical Research Centre, John Radcliffe Hospital , Oxford , United Kingdom |
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| Cites_doi | 10.1007/978-3-642-23808-6_10 10.1038/ncomms8119 10.1038/ng.2735 10.1128/AAC.00112-06 10.1186/1756-0500-4-504 10.1038/srep46327 10.1371/journal.pone.0033275 10.1056/NEJMoa1800474 10.3390/diagnostics9020049 10.1093/emph/eot003 10.1128/JCM.39.2.636-641.2001 10.1093/bioinformatics/bty949 10.1016/S1473-3099(15)00062-6 10.1186/s13073-015-0164-0 10.1093/bioinformatics/btx801 10.1007/978-3-642-33460-3_49 10.3389/fgene.2019.00922 10.1145/1014052.1014067 10.1164/rccm.201510-2091OC 10.1038/ng.3767 |
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| Copyright | Copyright © 2020 Kouchaki, Yang, Lachapelle, Walker, Walker, CRyPTIC Consortium, Peto, Crook and Clifton. Copyright © 2020 Kouchaki, Yang, Lachapelle, Walker, Walker, CRyPTIC Consortium, Peto, Crook and Clifton. 2020 Kouchaki, Yang, Lachapelle, Walker, Walker, CRyPTIC Consortium, Peto, Crook and Clifton |
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| Keywords | mutation ranking SLRF drug resistance MLRF tuberculosis |
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| License | Copyright © 2020 Kouchaki, Yang, Lachapelle, Walker, Walker, CRyPTIC Consortium, 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. |
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| References | Evgeniou (B6) 2004 Schleusener (B15) 2017; 7 Manson (B13) 2017; 49 Sechidis (B16) 2011 Deelder (B4) 2019; 10 Hazbón (B10) 2006; 50 Van Rie (B17) 2001; 39 Eldholm (B5) 2015; 6 Farhat (B8) 2016; 194 Georghiou (B9) 2012; 7 Walker (B18) 2015; 15 Zhang (B21) 2013; 45 Khan (B11) 2019; 9 Periwal (B14) 2011; 4 (B19) 2017 Borrell (B1) 2013; 2013 Kouchaki (B12) 2019; 35 Coll (B2) 2015; 7 (B3) 2018; 379 Yang (B20) 2018; 34 Faddoul (B7) 2012 |
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| Title | Multi-Label Random Forest Model for Tuberculosis Drug Resistance Classification and Mutation Ranking |
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