AIMC Topic: Antitubercular Agents
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Enhanced diagnosis of multi-drug-resistant microbes using group association modeling and machine learning.
Nature communications
Mar 25, 2025
New solutions are needed to detect genotype-phenotype associations involved in microbial drug resistance. Herein, we describe a Group Association Model (GAM) that accurately identifies genetic variants linked to drug resistance and mitigates false-po...
Prediction of tuberculosis treatment outcomes using biochemical makers with machine learning.
BMC infectious diseases
Feb 17, 2025
BACKGROUND: Tuberculosis (TB) continues to pose a significant threat to global public health. Enhancing patient prognosis is essential for alleviating the disease burden.
Artificial Intuition and accelerating the process of antimicrobial drug discovery.
Computers in biology and medicine
Feb 14, 2025
New drug development is a very challenging, expensive, and usually time-consuming process. This issue is very important with regard to antimicrobials, which are affected by the global issue of the development and spread of resistance. This framework ...
Deep learning-driven bacterial cytological profiling to determine antimicrobial mechanisms in .
Proceedings of the National Academy of Sciences of the United States of America
Feb 6, 2025
Tuberculosis (TB), caused by , remains a significant global health threat, affecting an estimated 10.6 million people in 2022. The emergence of multidrug resistant and extensively drug resistant strains necessitates the development of novel and effec...
Machine learning model to predict the adherence of tuberculosis patients experiencing increased levels of liver enzymes in Indonesia.
PloS one
Jan 24, 2025
Indonesia is still the second-highest tuberculosis burden country in the world. The antituberculosis adverse drug reaction and adherence may influence the success of treatment. The objective of this study is to define the model for predicting the adh...
Risk Prediction of Liver Injury in Pediatric Tuberculosis Treatment: Development of an Automated Machine Learning Model.
Drug design, development and therapy
Jan 13, 2025
PURPOSE: Drug-induced liver injury (DILI) is one of the most common and serious adverse drug reactions related to first-line anti-tuberculosis drugs in pediatric tuberculosis patients. This study aims to develop an automatic machine learning (AutoML)...
Efficient analysis of drug interactions in liver injury: a retrospective study leveraging natural language processing and machine learning.
BMC medical research methodology
Dec 20, 2024
BACKGROUND: Liver injury from drug-drug interactions (DDIs), notably with anti-tuberculosis drugs such as isoniazid, poses a significant safety concern. Electronic medical records contain comprehensive clinical information and have gained increasing ...
Isoniazid
Humans
Chemical and Drug Induced Liver Injury
Machine Learning
Antitubercular Agents
Retrospective Studies
Middle Aged
Drug Interactions
Case-Control Studies
Drug-Related Side Effects and Adverse Reactions
Logistic Models
Electronic Health Records
Natural Language Processing
Female
Adult
Male
Hyb_SEnc: An Antituberculosis Peptide Predictor Based on a Hybrid Feature Vector and Stacked Ensemble Learning.
IEEE/ACM transactions on computational biology and bioinformatics
Dec 10, 2024
Tuberculosis has plagued mankind since ancient times, and the struggle between humans and tuberculosis continues. Mycobacterium tuberculosis is the leading cause of tuberculosis, infecting nearly one-third of the world's population. The rise of pepti...
Machine learning-based prediction of antibiotic resistance in Mycobacterium tuberculosis clinical isolates from Uganda.
BMC infectious diseases
Dec 5, 2024
BACKGROUND: Efforts toward tuberculosis management and control are challenged by the emergence of Mycobacterium tuberculosis (MTB) resistance to existing anti-TB drugs. This study aimed to explore the potential of machine learning algorithms in predi...
Drug Resistance, Bacterial
Female
Young Adult
Adult
Male
Middle Aged
Microbial Sensitivity Tests
Uganda
Humans
Machine Learning
Streptomycin
Whole Genome Sequencing
Polymorphism, Single Nucleotide
Mycobacterium tuberculosis
Antitubercular Agents
Isoniazid
South Africa
Rifampin
Tuberculosis, Multidrug-Resistant
Integrated machine learning and physics-based methods assisted de novo design of Fatty Acyl-CoA synthase inhibitors.
Expert opinion on drug discovery
Nov 25, 2024
BACKGROUND: Tuberculosis is an infectious disease that has become endemic worldwide. The causative bacteria (Mtb) is targeted via several exciting drug targets. One newly discovered target is the Fatty Acyl-CoA synthase, which plays a significant ro...
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