ArticleScientific reports2020
PARGT: a software tool for predicting antimicrobial resistance in bacteria.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed, 47 citations in OpenAlex.
- Artificial Intelligence in Infectious Diseases and Clinical Microbiology.Infectious diseases & clinical microbiology · 2026Review
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- TolRad, a model for predicting radiation tolerance using Pfam annotations, identifies novel radiosensitive bacterial species from reference genomes and MAGs.Microbiology spectrum · 2024Article
- Whole Genome Sequence Analysis ofMicroorganisms · 2024Article
- Assessing computational predictions of antimicrobial resistance phenotypes from microbial genomes.Briefings in bioinformatics · 2024Article
- Enhancing Clinical Utility: Utilization of International Standards and Guidelines for Metagenomic Sequencing in Infectious Disease Diagnosis.International journal of molecular sciences · 2024Review
- Editorial: Machine learning approaches to antimicrobials: discovery and resistance.Frontiers in bioinformatics · 2024Article
- The Pseudomonas aeruginosa Resistome: Permanent and Transient Antibiotic Resistance, an Overview.Methods in molecular biology (Clifton, N.J.) · 2024Article
- Artificial intelligence tools for the identification of antibiotic resistance genes.Frontiers in microbiology · 2024Review
- PLM-ARG: antibiotic resistance gene identification using a pretrained protein language model.Bioinformatics (Oxford, England) · 2023Article
- Genomic Characterization ofPathogens (Basel, Switzerland) · 2023Article
- Employing Supervised Algorithms for the Prediction of Nanomaterial's Antioxidant Efficiency.International journal of molecular sciences · 2023Review
- Enhancing predictions of antimicrobial resistance of pathogens by expanding the potential resistance gene repertoire using a pan-genome-based feature selection approach.BMC bioinformatics · 2022Article
- Antibiotic Discovery and Resistance: The Chase and the Race.Antibiotics (Basel, Switzerland) · 2022Review
- Paving the way for precise diagnostics of antimicrobial resistant bacteria.Frontiers in molecular biosciences · 2022Review
- Editorial: Chemoinformatics Approaches to Structure- and Ligand-Based Drug Design, Volume II.Frontiers in pharmacology · 2022Article
- Antibiotic resistance: Time of synthesis in a post-genomic age.Computational and structural biotechnology journal · 2021Review
- Better understanding and prediction of antiviral peptides through primary and secondary structure feature importance.Scientific reports · 2020Article
- Amino AcidBiology · 2020Article
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
With the ever-increasing availability of whole-genome sequences, machine-learning approaches can be used as an alternative to traditional alignment-based methods for identifying new antimicrobial-resistance genes. Such approaches are especially helpful when pathogens cannot be cultured in the lab. In previous work, we proposed a game-theory-based feature evaluation algorithm. When using the protein characteristics identified by this algorithm, called 'features' in machine learning, our model accurately identified antimicrobial resistance (AMR) genes in Gram-negative bacteria. Here we extend our study to Gram-positive bacteria showing that coupling game-theory-identified features with machine learning achieved classification accuracies between 87% and 90% for genes encoding resistance to the antibiotics bacitracin and vancomycin. Importantly, we present a standalone software tool that implements the game-theory algorithm and machine-learning model used in these studies.
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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.