ArticleScientific reports2020
Artificial Intelligence and Machine learning based prediction of resistant and susceptible mutations in Mycobacterium tuberculosis.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 39 papers.
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Who cites it
39 citing papers in PubMed.
- Artificial Intelligence for Tuberculosis Screening and Detection: From Evidence to Policy and Implementation.Diagnostics (Basel, Switzerland) · 2026Review
- Machine learning-based analysis of drug resistance mutations in Mycobacterium tuberculosis.PloS one · 2026Article
- Future Research Directions on Mycobacterium tuberculosis Proteins.Advances in experimental medicine and biology · 2026Review
- Comprehensive Approaches to Protein Detection and Analysis in Mycobacterium tuberculosis.Advances in experimental medicine and biology · 2026Review
- Development of machine learning models to identify potentially active compounds against tuberculosis.Scientific reports · 2025Article
- Artificial intelligence for screening drug resistance in tuberculosis.The Indian journal of medical research · 2025Article
- Gut microbiota and tuberculosis.iMeta · 2025Review
- Integration of AI and ML in Tuberculosis (TB) Management: From Diagnosis to Drug Discovery.Diseases (Basel, Switzerland) · 2025Review
- The Recent Development in the Diagnosis ofSmart medicine · 2025Review
- Predicting and improving diagnosis of tuberculosis outcomes in South Africa using machine learning techniques.PLOS global public health · 2025Article
- Can Artificial Intelligence Enhance the Antimicrobial Effectiveness of Photodynamic Therapy? What We Know and What We Do Not Know?Journal of lasers in medical sciences · 2025Review
- Machine learning-based approach for identification of new resistance associated mutations from whole genome sequences ofBioinformatics advances · 2025Article
- Detecting antibiotic resistance: classical, molecular, advanced bioengineering, and AI-enhanced approaches.Frontiers in microbiology · 2025Review
- Rapid and accurate recognition of erythrocytic stage parasites ofFrontiers in microbiology · 2025Article
- Quantitative drug susceptibility testing for Mycobacterium tuberculosis using unassembled sequencing data and machine learning.PLoS computational biology · 2024Article
- Four Transformer-Based Deep Learning Classifiers Embedded with an Attention U-Net-Based Lung Segmenter and Layer-Wise Relevance Propagation-Based Heatmaps for COVID-19 X-ray Scans.Diagnostics (Basel, Switzerland) · 2024Article
- Digital health delivery in respiratory medicine: adjunct, replacement or cause for division?European respiratory review : an official journal of the European Respiratory Society · 2024Review
- Review
- Tackling the Antimicrobial Resistance "Pandemic" with Machine Learning Tools: A Summary of Available Evidence.Microorganisms · 2024Review
- Push forward LC-MS-based therapeutic drug monitoring and pharmacometabolomics for anti-tuberculosis precision dosing and comprehensive clinical management.Journal of pharmaceutical analysis · 2024Review
Corrections and comments
- Erratum issued
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Tuberculosis (TB), an infectious disease caused by Mycobacterium tuberculosis (M.tb), causes highest number of deaths globally for any bacterial disease necessitating novel diagnosis and treatment strategies. High-throughput sequencing methods generate a large amount of data which could be exploited in determining multi-drug resistant (MDR-TB) associated mutations. The present work is a computational framework that uses artificial intelligence (AI) based machine learning (ML) approaches for predicting resistance in the genes rpoB, inhA, katG, pncA, gyrA and gyrB for the drugs rifampicin, isoniazid, pyrazinamide and fluoroquinolones. The single nucleotide variations were represented by several sequence and structural features that indicate the influence of mutations on the target protein coded by each gene. We used ML algorithms - naïve bayes, k nearest neighbor, support vector machine, and artificial neural network, to build the prediction models. The classification models had an average accuracy of 85% across all examined genes and were evaluated on an external unseen dataset to demonstrate their application. Further, molecular docking and molecular dynamics simulations were performed for wild type and predicted resistance causing mutant protein and anti-TB drug complexes to study their impact on the conformation of proteins to confirm the observed phenotype.
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