Discover Community Create You

Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

Tuberculosis drug resistance profiling based on machine learning: A literature review

1 Jan 2022

Abstract excerpt

Tuberculosis (TB), caused by Mycobacterium tuberculosis (MTB), is one of the top 10 causes of death worldwide. Drug-resistant tuberculosis (DR-TB) poses a major threat to the World Health Organization's "End TB" strategy which has defined its target as the year 2035. In 2019, there were close to 0.5 million cases of DRTB, of which 78% were resistant to multiple TB drugs. The traditional culture-based drug...

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to Computational Drug Discovery Methods
Share to Machine Learning in Bioinformatics
Share to Tuberculosis Research and Epidemiology

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Source

Identifiers

PMID: 35176257
DOI: 10.1016/j.bjid.2022.102332

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 7 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Tuberculosis drug resistance profiling based on machine learning: A literature reviewPMID 35176257Read ↗

Machine Learning Predicts Accurately Mycobacterium tuberculosis Drug Resistance From Whole Genome Sequencing DataSemantic proximity Read ↗

Machine learning-based analysis of drug resistance mutations in Mycobacterium tuberculosis.Semantic proximity Read ↗

Machine Learning for Classifying Tuberculosis Drug-Resistance from DNA Sequencing DataSemantic proximity Read ↗

Study and implementation of a new machine learning algorithm to predict drug resistance in Mycobacterium tuberculosis complex clinical isolatesSemantic proximity Read ↗

TB-Bench: A Systematic Benchmark of Machine Learning and Deep Learning Methods for Second-Line TB Drug Resistance PredictionSemantic proximity Read ↗

Leveraging large-scale Mycobacterium tuberculosis whole genome sequence data to characterise drug-resistant mutations using machine learning and statistical approaches.Semantic proximity Read ↗

Machine learning-based prediction of antibiotic resistance in Mycobacterium tuberculosis clinical isolates from Uganda.Semantic proximity Read ↗

Accurate and rapid prediction of tuberculosis drug resistance from genome sequence data using traditional machine learning algorithms and CNN.Semantic proximity Read ↗

GenTB: A user-friendly genome-based predictor for tuberculosis resistance powered by machine learningSemantic proximity Read ↗

GenTB: A user-friendly genome-based predictor for tuberculosis resistance powered by machine learningSemantic proximity Read ↗

Clinical implications of molecular drug resistance testing for Mycobacterium tuberculosis: a 2023 TBnet/RESIST-TB consensus statement.Semantic proximity Read ↗

Expanding Tuberculosis Drug Resistance Prediction beyond binary: Deep Learning for Minimum Inhibitory Concentration predictionSemantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.