ArticleComputational and structural biotechnology journal2024
Unitig-centered pan-genome machine learning approach for predicting antibiotic resistance and discovering novel resistance genes in bacterial strains.
Article in Computational and structural biotechnology journal, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Alignment-freeJournal of clinical microbiology · 2026Article
- Alignment-Free Guided Design of a Pan-Orthoflavivirus RT-qPCR Assay.bioRxiv : the preprint server for biology · 2026Article
- Beyond Random Splits: Assessing the Generalization of Graph and Vector Models for WT-Structure-Only Drug Resistance Prediction under Protein-Disjoint Evaluation.Computational and structural biotechnology journal · 2026Article
- Advances in the detection of Drug-Resistant bacteria: current trends and innovations.European journal of clinical microbiology & infectious diseases : official publication of the European Society of Clinical Microbiology · 2026Review
- Artificial intelligence-enhanced biosurveillance for antimicrobial resistance in sub-Saharan Africa.International health · 2025Review
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In current genomic research, the widely used methods for predicting antimicrobial resistance (AMR) often rely on prior knowledge of known AMR genes or reference genomes. However, these methods have limitations, potentially resulting in imprecise predictions owing to incomplete coverage of AMR mechanisms and genetic variations. To overcome these limitations, we propose a pan-genome-based machine learning approach to advance our understanding of AMR gene repertoires and uncover possible feature sets for precise AMR classification. By building compacted de Brujin graphs (cDBGs) from thousands of genomes and collecting the presence/absence patterns of unique sequences (unitigs) for
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Registered trials
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