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Related Experiment Video

Updated: Jun 2, 2026

Implementation of In Vitro Drug Resistance Assays: Maximizing the Potential for Uncovering Clinically Relevant Resistance Mechanisms
08:46

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Published on: December 9, 2015

A Novel Bioinformatics Pipeline and a Machine-Learning Approach for Antimicrobial Resistance Phenotypic Prediction.

Owen Visser1, Victor Agboli1, Somnath Datta1

  • 1Department of Biostatistics, University of Florida, Gainesville, USA.

Bioinformatics and Biology Insights
|June 1, 2026
PubMed
Summary
This summary is machine-generated.

Antimicrobial drug overuse fuels bacterial resistance. This study developed a scalable pipeline to predict antimicrobial resistance using diverse sequencing data from public archives, achieving 80.2% accuracy in external testing.

Keywords:
antimicrobial resistance predictionbioinformatics pipelinephenotypic prediction

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Last Updated: Jun 2, 2026

Implementation of In Vitro Drug Resistance Assays: Maximizing the Potential for Uncovering Clinically Relevant Resistance Mechanisms
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Isolation and Identification of Waterborne Antibiotic-Resistant Bacteria and Molecular Characterization of their Antibiotic Resistance Genes
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Isolation and Identification of Waterborne Antibiotic-Resistant Bacteria and Molecular Characterization of their Antibiotic Resistance Genes

Published on: March 3, 2023

Area of Science:

  • Genomics
  • Microbiology
  • Bioinformatics

Background:

  • Antimicrobial drug overuse is a significant driver of increasing bacterial resistance.
  • This resistance compromises the efficacy of existing and future antimicrobial treatments.
  • Publicly available sequencing data offers a valuable resource for studying antimicrobial resistance globally.

Purpose of the Study:

  • To develop and validate a scalable computational pipeline for predicting antimicrobial resistance.
  • To analyze a large dataset of globally sourced bacterial isolates from public sequencing archives.
  • To assess the performance of machine learning classifiers in predicting antimicrobial resistance from heterogeneous sequencing data.

Main Methods:

  • A pipeline was developed to process 10,803 bacterial isolates from Sequence Read Archive (SRA) datasets.
  • Data preprocessing included quality control, trimming, decontamination, and mapping to resistance gene and strain-level genome libraries.
  • Three machine learning classifiers (logistic regression, random forest, extreme gradient boosting) were trained and combined in an ensemble model.

Main Results:

  • The developed pipeline achieved 83.8% average balanced accuracy during internal training and 80.2% during external testing.
  • Variable importance analysis identified known antimicrobial resistance gene classes and specific bacterial strain markers.
  • The results confirm the biological relevance of identified markers, including *Acinetobacter baumannii* and *Campylobacter jejuni* strains.

Conclusions:

  • The study demonstrates a scalable and effective approach for predicting antimicrobial resistance using heterogeneous, publicly available sequencing data.
  • The pipeline successfully leverages diverse sequencing platforms and protocols for resistance prediction.
  • This work provides a foundation for global surveillance and understanding of antimicrobial resistance patterns.