Machine learning for classifying tuberculosis drug-resistance from DNA sequencing data-Reference-Cited by-同舟云学术

Machine learning for classifying tuberculosis drug-resistance from DNA sequencing data

Published:2017-12-12 Issue:10 Volume:34 Page:1666-1671
ISSN:1367-4803
Container-title:Bioinformatics
language:en
Short-container-title:

Author:

Yang Yang¹,Niehaus Katherine E¹,Walker Timothy M²,Iqbal Zamin²,Walker A Sarah²³,Wilson Daniel J²,Peto Tim E A²⁴,Crook Derrick W²³⁴,Smith E Grace⁵,Zhu Tingting¹,Clifton David A¹

Affiliation:

1. Department of Engineering Science, Institute of Biomedical Engineering, University of Oxford, Oxford, UK

2. Nuffield Department of Medicine, University of Oxford, Oxford, UK

3. NIHR Oxford Biomedical Research Centre, Oxford, UK

4. National Infection Service, Public Health England, Colindale, London, UK

5. Public Health England, Colindale, London, UK

Funder

Royal Academy of Engineering

EPSRC

Wellcome Trust

NIHR

Royal Society

Bill & Melinda Gates Foundation

Publisher

Oxford University Press (OUP)

Subject

Computational Mathematics,Computational Theory and Mathematics,Computer Science Applications,Molecular Biology,Biochemistry,Statistics and Probability

Link

http://academic.oup.com/bioinformatics/article-pdf/34/10/1666/25118199/btx801.pdf

Reference17 articles.

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2. Phytb: Phylogenetic tree visualisation and sample positioning for M. tuberculosis;Benavente;BMC Bioinformatics,2015

3. Evolution and transmission of drug resistant tuberculosis in a Russian population;Casali;Nat. Genet,2014

4. Evolution of extensively drug-resistant tuberculosis over four decades: whole genome sequencing and dating analysis of Mycobacterium tuberculosis isolates from KwaZulu-Natal;Cohen;PLoS Med,2015

5. Rapid determination of anti-tuberculosis drug resistance from whole-genome sequences;Coll;Genome Med,2015

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