Application of machine learning techniques to tuberculosis drug resistance analysis
- Submitting institution
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The University of Surrey
- Unit of assessment
- 12 - Engineering
- Output identifier
- 9027939_1
- Type
- D - Journal article
- DOI
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10.1093/bioinformatics/bty949
- Title of journal
- Bioinformatics
- Article number
- -
- First page
- 2276
- Volume
- 35
- Issue
- 13
- ISSN
- 1367-4803
- Open access status
- Compliant
- Month of publication
- -
- Year of publication
- 2018
- URL
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-
- Supplementary information
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- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
-
- Research group(s)
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-
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Timely identification of Mycobacterium tuberculosis (MTB) resistance to existing drugs is vital to decrease mortality and prevent the amplification of existing antibiotic resistance. Machine learning (ML) methods have been widely applied, but they have not previously been validated on a large cohort of MTB samples from multi-centres. Several ML classifiers and linear dimension reduction techniques were developed and compared for a cohort of 13402 isolates collected from 16 countries across six continents and 11 drugs. Several possible novel resistance markers were identified. The work was supported by CRyPTIC to improve tuberculosis control and WHO's TB strategy for better/faster/more targeted treatment.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -