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Journal article

Application of machine learning techniques to tuberculosis drug resistance analysis

Abstract:
Motivation 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 methods have been widely applied for timely predicting resistance of MTB given a specific drug and identifying resistance markers. However, they have been not validated on a large cohort of MTB samples from multi-centers across the world in terms of resistance predicti... Expand abstract
Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1093/bioinformatics/bty949

Authors


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Institution:
University of Oxford
Division:
MPLS Division
Department:
Engineering
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS Division
Department:
Engineering
Role:
Author
More by this author
Institution:
University of Oxford
Division:
Medical Sciences Division
Department:
NDM
Sub department:
NDM Experimental Medicine
Role:
Author
ORCID:
0000-0003-0421-9264
More by this author
Institution:
University of Oxford
Division:
MSD
Department:
NDM
Sub department:
NDM Experimental Medicine
Role:
Author
ORCID:
0000-0002-0412-8509
More by this author
Institution:
University of Oxford
Division:
Medical Sciences Division
Department:
NDM
Sub department:
NDM Experimental Medicine
Role:
Author
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More from this funder
Funding agency for:
Wilson, D
Grant:
101237/Z/13/Z
Publisher:
Oxford University Press Publisher's website
Journal:
Bioinformatics Journal website
Volume:
35
Issue:
13
Pages:
2276–2282
Publication date:
2018-11-21
Acceptance date:
2018-11-14
DOI:
EISSN:
1367-4811
ISSN:
1367-4803
Pmid:
30462147
Language:
English
Pubs id:
pubs:946190
UUID:
uuid:9d278dc8-5074-4f6e-9b5e-c218e805727e
Local pid:
pubs:946190
Deposit date:
2018-12-01

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