A pan-genome-based machine learning approach for predicting antimicrobial resistance activities of the Escherichia coli strains

Hsuan Lin Her, Yu Wei Wu

Research output: Contribution to journalArticle

2 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)i89-i95
JournalBioinformatics
Volume34
Issue number13
DOIs
Publication statusPublished - Jul 1 2018

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Escherichia coli
Escherichia Coli
Learning systems
Machine Learning
Genome
Genes
Antibiotics
Gene
Anti-Bacterial Agents
Accessories
Multigene Family
Drugs
Resistance
Genetic Algorithm
Developed Countries
Pharmaceutical Preparations
Developing Countries
Performance Prediction
Genetic algorithms
Inspection

ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics

Cite this

A pan-genome-based machine learning approach for predicting antimicrobial resistance activities of the Escherichia coli strains. / Her, Hsuan Lin; Wu, Yu Wei.

In: Bioinformatics, Vol. 34, No. 13, 01.07.2018, p. i89-i95.

Research output: Contribution to journalArticle

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