An explainable machine learning platform for pyrazinamide resistance prediction and genetic feature identification of Mycobacterium tuberculosis. (20th November 2020)

• Record Type: Journal Article
• Authors: Zhang, Andrew; Teng, Ling; Alterovitz, Gil
• Is Part Of: Journal of the American Medical Informatics Association. Volume 28:Number 3(2021)
• Page Start: 533
• Page End: 540
• Publication Date: 2020-11-20
• Subjects: antimicrobial resistance -- machine learning -- explainable AI; Medical informatics -- Periodicals; Information Services -- Periodicals; Medical Informatics -- Periodicals; Médecine -- Informatique -- Périodiques; Informatica; Geneeskunde; Informatique médicale; Computer network resources; Electronic journals; 610.285
• DOI: 10.1093/jamia/ocaa233
• Languages: English
• ISSNs: 1067-5027

Abstract

Abstract: Objective: Tuberculosis 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 Methods: We 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. Results: The 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. Conclusions: Machine learning can be used to achieve high-accuracy resistance prediction, and identify genes and mutations causing the resistance.