Machine learning and phylogenetic analysis allow for predicting antibiotic resistance in M. tuberculosis

Albert Yurtseven (Corresponding author), Sofia Buyanova, Amay Ajaykumuar Agrawal, Olga Bochkareva, Olga Kalinina

Publications: Contribution to journalArticlePeer Reviewed

Abstract

Background: Antimicrobial resistance (AMR) poses a significant global health threat, and an accurate prediction of bacterial resistance patterns is critical for effective treatment and control strategies. In recent years, machine learning (ML) approaches have emerged as powerful tools for analyzing large-scale bacterial AMR data. However, ML methods often ignore evolutionary relationships among bacterial strains, which can greatly impact performance of the ML methods, especially if resistance-associated features are attempted to be detected. Genome-wide association studies (GWAS) methods like linear mixed models accounts for the evolutionary relationships in bacteria, but they uncover only highly significant variants which have already been reported in literature. Results: In this work, we introduce a novel phylogeny-related parallelism score (PRPS), which measures whether a certain feature is correlated with the population structure of a set of samples. We demonstrate that PRPS can be used, in combination with SVM- and random forest-based models, to reduce the number of features in the analysis, while simultaneously increasing models’ performance. We applied our pipeline to publicly available AMR data from PATRIC database for Mycobacterium tuberculosis against six common antibiotics. Conclusions: Using our pipeline, we re-discovered known resistance-associated mutations as well as new candidate mutations which can be related to resistance and not previously reported in the literature. We demonstrated that taking into account phylogenetic relationships not only improves the model performance, but also yields more biologically relevant predicted most contributing resistance markers.

Original languageEnglish
Article number404
Number of pages10
JournalBMC Microbiology
Volume23
Issue number1
Early online dateSep 2023
DOIs
Publication statusPublished - 20 Dec 2023

Austrian Fields of Science 2012

  • 106026 Ecosystem research
  • 106022 Microbiology

Keywords

  • mashine learning
  • phylogeny
  • antimicrobial restistance
  • tuberculosis
  • Tuberculosis
  • Machine learning
  • Antimicrobial resistance
  • Phylogeny

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