Application of machine learning techniques to tuberculosis drug resistance analysis | Zendy

Samaneh Kouchaki | Zendy; Yang Yang | Zendy; Timothy M. Walker | Zendy; Daniel J. Wilson | Zendy; Tim Peto | Zendy; Derrick W. Crook | Zendy; David A. Clifton | Zendy

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Application of machine learning techniques to tuberculosis drug resistance analysis

Author(s) -

Samaneh Kouchaki,

Yang Yang,

Timothy M. Walker,

Daniel J. Wilson,

Tim Peto,

Derrick W. Crook,

David A. Clifton

Publication year - 2018

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/bty949

Subject(s) - identification (biology) , tuberculosis , drug resistance , mycobacterium tuberculosis , machine learning , antibiotic resistance , cohort , artificial intelligence , resistance (ecology) , medicine , computer science , antibiotics , biology , microbiology and biotechnology , pathology , ecology , botany

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 prediction and resistance marker identification. Several machine learning classifiers and linear dimension reduction techniques were developed and compared for a cohort of 13 402 isolates collected from 16 countries across 6 continents and tested 11 drugs.

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