ArticleJournal of the American Medical Informatics Association : JAMIA2021
An explainable machine learning platform for pyrazinamide resistance prediction and genetic feature identification of Mycobacterium tuberculosis.
Article in Journal of the American Medical Informatics Association : JAMIA, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 20 citations in OpenAlex.
- Predicting rifampicin resistance inERJ open research · 2025Article
- Machine learning-based prediction of antibiotic resistance in Mycobacterium tuberculosis clinical isolates from Uganda.BMC infectious diseases · 2024Article
- Tackling the Antimicrobial Resistance "Pandemic" with Machine Learning Tools: A Summary of Available Evidence.Microorganisms · 2024Review
- Artificial intelligence tools for the identification of antibiotic resistance genes.Frontiers in microbiology · 2024Review
- Machine Learning of the Whole Genome Sequence ofMicroorganisms · 2023Review
- Advances in computational frameworks in the fight against TB: The way forward.Frontiers in pharmacology · 2023Review
- Characterizing Thrombotic Complication Risk Factors Associated With COVID-19 via Heterogeneous Patient Data: Retrospective Observational Study.Journal of medical Internet research · 2022Observational
- Accurate and rapid prediction of tuberculosis drug resistance from genome sequence data using traditional machine learning algorithms and CNN.Scientific reports · 2022Article
- Predictions, Pivots, and a Pandemic: a Review of 2020's Top Translational Bioinformatics Publications.Yearbook of medical informatics · 2021Review
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
Abstract
objectiveTuberculosis is the leading cause of death from a single infectious agent. The emergence of antimicrobial resistant Mycobacterium tuberculosis strains makes the problem more severe. Pyrazinamide (PZA) is an important component for short-course treatment regimens and first- and second-line treatment regimens. This research aims for fast diagnosis of M. tuberculosis resistance to PZA and identification of genetic features causing resistance. MATERIALS AND
methodsWe use clinically collected genomic data of M. tuberculosis that are resistant or susceptible to PZA. A machine learning platform is built to diagnose PZA resistance using the whole genome sequence data, and to identify resistance genes and mutations. The platform consists of a deep convolutional neural network (DCNN) model for resistance diagnosis and a support vector machine (SVM) model as a surrogate to identify resistance genes and mutations.
resultsThe DCNN model achieves a PZA resistance diagnosis accuracy of 93%. Each prediction takes less than a second. The SVM has revealed 2 novel genes, embB and gyrA, besides the well-known pncA gene, and 9 mutations that harbor PZA resistance. DISCUSSION: The DCNN and SVM machine learning platform, if used together with the real-time genome sequencing machines, could allow for rapid PZA diagnosis, allowing for critical time to ensure good patient outcomes, and preventing outbreaks of deadly infections. Furthermore, identifying pertinent resistance genes and mutations will help researchers better understand the biological mechanisms behind resistance.
conclusionsMachine learning can be used to achieve high-accuracy resistance prediction, and identify genes and mutations causing the resistance.
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