ArticleNature communications2020
A biochemically-interpretable machine learning classifier for microbial GWAS.
Article in Nature communications, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
41 citing papers in PubMed.
- Combating antimicrobial resistance through multimodal data via artificial intelligence.One health (Amsterdam, Netherlands) · 2026Review
- Artificial Intelligence in Rapid Diagnostics for Antimicrobial Resistance: From Bench to Bedside.Health science reports · 2026Article
- KEGG orthology-based machine learning reveals functional determinants of antimicrobial resistance inMicrobiology spectrum · 2026Article
- Predicting growth, water-use efficiency and drought response through machine learning, GWAS and differential expression in Ponderosa pine.BMC plant biology · 2026Article
- Genome-wide association study bridging genomics-phenomics gap in natural plant populations.Journal of applied genetics · 2026Review
- Whole-genome phenotype prediction with machine learning: open problems in bacterial genomics.Bioinformatics (Oxford, England) · 2025Article
- Review
- Article
- Reliable machine learning models in genomic medicine using conformal prediction.Frontiers in bioinformatics · 2025Review
- Catalase activity deficiency sensitizes multidrug-resistant Mycobacterium tuberculosis to the ATP synthase inhibitor bedaquiline.Nature communications · 2024Article
- Tackling the Antimicrobial Resistance "Pandemic" with Machine Learning Tools: A Summary of Available Evidence.Microorganisms · 2024Review
- Convergence of resistance and evolutionary responses in Escherichia coli and Salmonella enterica co-inhabiting chicken farms in China.Nature communications · 2024Article
- Global pathogenomic analysis identifies known and candidate genetic antimicrobial resistance determinants in twelve species.Nature communications · 2023Article
- The limitations of phenotype prediction in metabolism.PLoS computational biology · 2023Article
- Machine learning and metagenomics reveal shared antimicrobial resistance profiles across multiple chicken farms and abattoirs in China.Nature food · 2023Article
- Machine Learning of the Whole Genome Sequence ofMicroorganisms · 2023Review
- Understanding Antimicrobial Resistance Using Genome-Scale Metabolic Modeling.Antibiotics (Basel, Switzerland) · 2023Review
- Artificial Intelligence for Antimicrobial Resistance Prediction: Challenges and Opportunities towards Practical Implementation.Antibiotics (Basel, Switzerland) · 2023Review
- Whole-genome sequencing and genome-scale metabolic modeling of Chromohalobacter canadensis 85B to explore its salt tolerance and biotechnological use.MicrobiologyOpen · 2022Article
- Machine Learning for Antimicrobial Resistance Prediction: Current Practice, Limitations, and Clinical Perspective.Clinical microbiology reviews · 2022Review
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Current machine learning classifiers have successfully been applied to whole-genome sequencing data to identify genetic determinants of antimicrobial resistance (AMR), but they lack causal interpretation. Here we present a metabolic model-based machine learning classifier, named Metabolic Allele Classifier (MAC), that uses flux balance analysis to estimate the biochemical effects of alleles. We apply the MAC to a dataset of 1595 drug-tested Mycobacterium tuberculosis strains and show that MACs predict AMR phenotypes with accuracy on par with mechanism-agnostic machine learning models (isoniazid AUC = 0.93) while enabling a biochemical interpretation of the genotype-phenotype map. Interpretation of MACs for three antibiotics (pyrazinamide, para-aminosalicylic acid, and isoniazid) recapitulates known AMR mechanisms and suggest a biochemical basis for how the identified alleles cause AMR. Extending flux balance analysis to identify accurate sequence classifiers thus contributes mechanistic insights to GWAS, a field thus far dominated by mechanism-agnostic results.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.