MuseAMR, a multimodal, multi-label deep-learning framework that predicts both individual-drug resistance and clinically actionable composite phenotypes from Mycobacterium tuberculosis genomes, shows robust cross-regional performance and few-shot adaptation to emerging drugs, supporting its potential for regimen-level tuberculosis resistance profiling and surveillance.
This critical narrative review evaluates how these approaches may improve susceptibility prediction for multidrug- and rifampicin-resistant tuberculosis, where their evidence is strongest, and which methodological and implementation barriers limit clinical adoption.
Bako Helen Yakubu, Ajide Bukola Adeyoola, Olokun Alexander Lanzema· International Journal of Pat...· 0 citations
This study provides a thorough examination of bioinformatics research, highlighting how the integration of genomic, proteomic and metabolomic data into TB research has revolutionised the way TB is studied, particularly in understanding resistance mechanisms, identifying new therapeutic targets, and expediting the devel...
Ahmed Mohammed Hayawi· Modern Journal of Health and...· 0 citations
This study highlights the potential of integrating protein-level mutation data with interpretable machine learning models for rapid, in-silico prediction of MDR-TB, offering a cost-effective approach to resistance screening.
Theja K. V., Ram Shankar Barai, U. D. Ranganathan et al.· The Indian journal of medica...· 0 citations
The emergence of drug resistance in Mycobacterium tuberculosis (M.tb) presents a major challenge to drug discovery, as compounds active against drug-susceptible (DS) isolates may not retain comparable inhibitory activity against drug-resistant (DR) isolates. Here, we developed an AI/ML workflow to independently priorit...
Antimicrobial resistance (AMR) is a complex One Health challenge driven by the interconnected emergence, selection, persistence, and dissemination of resistance across clinical, animal, agricultural, food, and environmental microbiomes. Resistome profiling through whole-genome and metagenomic sequencing has transformed...
Christian Joseph N. Ong, R. Nazari, J. T. P. Cruz· Discover Artificial Intellig...· 0 citations
This work identifies global patterns in MDR evolutionary pathways, remarkably shared across multiple ESKAPEE species, and infer these evolutionary pathways across ESKAPEE pathogens using evolutionary accumulation modelling (EvAM), an emerging class of machine learning methods with roots in cancer progression.
Olav N. L. Aga, Sabrina J. Moyo, Jakob Fernø et al.· bioRxiv· 0 citations
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