ArticleBioinformatics (Oxford, England)2018
A pan-genome-based machine learning approach for predicting antimicrobial resistance activities of the Escherichia coli strains.
Article in Bioinformatics (Oxford, England), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 56 papers, 2 of them syntheses that pooled it.
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Who cites it
56 citing papers in PubMed, 2 syntheses or guidelines pooled it, 103 citations in OpenAlex.
- Antimicrobial resistance crisis: could artificial intelligence be the solution?Military Medical Research · 2024Pooled it
- Pooled it
- Comparison of Machine Learning Classifiers for Predicting Reported Antibiotic Treatment After Recent Fever/Cough Among Under-Five Children: A Pooled Analysis of DHS Data From 64 LMICs.Public health challenges · 2026Article
- Integrative Modular, Network-Based, and Machine Learning Framework for Predicting Accessory Genome Functions and Virulence in Escherichia coli O157:H7.MicrobiologyOpen · 2026Article
- Integrating artificial intelligence with genome sequencing against antimicrobial resistance: a narrative review.Frontiers in public health · 2026Review
- StaLAENet: A stacked LSTM-nested deep-autoencoder network for identification of antimicrobial resistance of nosocomial pathogens.Journal of biosciences · 2026Article
- Assessment for antibiotic resistance inVirulence · 2025Article
- Characterization of antibiotic resistance and biofilm formation in clinical Helicobacter pylori isolates from Ningxia, China.Scientific reports · 2025Article
- Classifying Convergences in the Light of Horizontal Gene Transfer: Epaktovars and Xenotypes.Molecular biology and evolution · 2025Article
- Artificial intelligence in drug resistance management.3 Biotech · 2025Review
- PanARGMiner (Pan-Genomic Antimicrobial Resistance Gene Miner): An advanced feature selection framework for extracting key resistance genes from pan-genomic datasets.Computational and structural biotechnology journal · 2025Article
- Unitig-centered pan-genome machine learning approach for predicting antibiotic resistance and discovering novel resistance genes in bacterial strains.Computational and structural biotechnology journal · 2024Article
- The Gene ClusterInternational journal of molecular sciences · 2024Article
- Article
- The Impact of Artificial Intelligence on Microbial Diagnosis.Microorganisms · 2024Review
- Tackling the Antimicrobial Resistance "Pandemic" with Machine Learning Tools: A Summary of Available Evidence.Microorganisms · 2024Review
- Assessing computational predictions of antimicrobial resistance phenotypes from microbial genomes.Briefings in bioinformatics · 2024Article
- Generalizability of machine learning in predicting antimicrobial resistance in E. coli: a multi-country case study in Africa.BMC genomics · 2024Article
- Pasa: leveraging population pangenome graph to scaffold prokaryote genome assemblies.Nucleic acids research · 2024Article
- Silicon versus Superbug: Assessing Machine Learning's Role in the Fight against Antimicrobial Resistance.Antibiotics (Basel, Switzerland) · 2023Review
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Motivation: Antimicrobial resistance (AMR) is becoming a huge problem in both developed and developing countries, and identifying strains resistant or susceptible to certain antibiotics is essential in fighting against antibiotic-resistant pathogens. Whole-genome sequences have been collected for different microbial strains in order to identify crucial characteristics that allow certain strains to become resistant to antibiotics; however, a global inspection of the gene content responsible for AMR activities remains to be done. Results: We propose a pan-genome-based approach to characterize antibiotic-resistant microbial strains and test this approach on the bacterial model organism Escherichia coli. By identifying core and accessory gene clusters and predicting AMR genes for the E. coli pan-genome, we not only showed that certain classes of genes are unevenly distributed between the core and accessory parts of the pan-genome but also demonstrated that only a portion of the identified AMR genes belong to the accessory genome. Application of machine learning algorithms to predict whether specific strains were resistant to antibiotic drugs yielded the best prediction accuracy for the set of AMR genes within the accessory part of the pan-genome, suggesting that these gene clusters were most crucial to AMR activities in E. coli. Selecting subsets of AMR genes for different antibiotic drugs based on a genetic algorithm (GA) achieved better prediction performances than the gene sets established in the literature, hinting that the gene sets selected by the GA may warrant further analysis in investigating more details about how E. coli fight against antibiotics. Supplementary information: Supplementary data are available at Bioinformatics online.
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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.