Machine Learning for Classifying Tuberculosis Drug-Resistance from DNA Sequencing Data.

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Predicting bacterial resistance from whole-genome sequences using k-mers and stability selection

P2860

Q58605130-CE86DEA2-3BE2-4F5F-B544-EA4FB8853048

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Machine Learning for Classifying Tuberculosis Drug-Resistance from DNA Sequencing Data.

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http://www.wikidata.org/entity/Q45942918

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2017 nî lūn-bûn

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2017年の論文

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2017年学术文章

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2017年学术文章

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2017年學術文章

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name

Machine Learning for Classifying Tuberculosis Drug-Resistance from DNA Sequencing Data.

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Machine Learning for Classifying Tuberculosis Drug-Resistance from DNA Sequencing Data.

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type

Item

label

Machine Learning for Classifying Tuberculosis Drug-Resistance from DNA Sequencing Data.

@en

Machine Learning for Classifying Tuberculosis Drug-Resistance from DNA Sequencing Data.

@nl

prefLabel

Machine Learning for Classifying Tuberculosis Drug-Resistance from DNA Sequencing Data.

@en

Machine Learning for Classifying Tuberculosis Drug-Resistance from DNA Sequencing Data.

@nl

P1476

Machine Learning for Classifying Tuberculosis Drug-Resistance from DNA Sequencing Data.

@en

P2093

A Sarah Walker

http://www.w3.org/2001/XMLSchema#string

Daniel J Wilson

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David A Clifton

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Derrick W Crook

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E Grace Smith

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Katherine E Niehaus

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Tim E A Peto

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Timothy M Walker

http://www.w3.org/2001/XMLSchema#string

Yang Yang

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P2860

Evaluation of genetic mutations associated with Mycobacterium tuberculosis resistance to amikacin, kanamycin and capreomycin: a systematic review

Evolution of Extensively Drug-Resistant Tuberculosis over Four Decades: Whole Genome Sequencing and Dating Analysis of Mycobacterium tuberculosis Isolates from KwaZulu-Natal

Two new rapid SNP-typing methods for classifying Mycobacterium tuberculosis complex into the main phylogenetic lineages

Genome sequencing of 161 Mycobacterium tuberculosis isolates from China identifies genes and intergenic regions associated with drug resistance.

PhyTB: Phylogenetic tree visualisation and sample positioning for M. tuberculosis

Whole-genome sequencing of rifampicin-resistant Mycobacterium tuberculosis strains identifies compensatory mutations in RNA polymerase genes.

Rapid determination of anti-tuberculosis drug resistance from whole-genome sequences

Whole-genome sequencing for prediction of Mycobacterium tuberculosis drug susceptibility and resistance: a retrospective cohort study.

Mycobacterium tuberculosis mutation rate estimates from different lineages predict substantial differences in the emergence of drug-resistant tuberculosis.

Genomic analysis identifies targets of convergent positive selection in drug-resistant Mycobacterium tuberculosis.

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1666-1671

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scholarly article

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10.1093/BIOINFORMATICS/BTX801

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English

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P478

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Zamin Iqbal

Tingting Zhu

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2018-05-01T00:00:00Z

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29240876

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P921

machine learning

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5946815

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Machine Learning for Classifying Tuberculosis Drug-Resistance from DNA Sequencing Data.

about

P2860

P2860

Machine Learning for Classifying Tuberculosis Drug-Resistance from DNA Sequencing Data.

description

name

type

label

prefLabel

P1433

P1476

P2093

P2860

P304

P31

P356

P407

P433

P478

P50

P577

P698

P921

P932

P356

P1433

P1476

P2093

P2860

P304

P31

P356

P407

P433

P478

P50

P577

P698

P921

P932