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Updated: Aug 20, 2025

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A Facile machine learning multi-classification model for Streptococcus agalactiae clonal complexes.

Jingxian Liu1, Jing Zhao1, Chencui Huang2

  • 1Department of Clinical Laboratory, Xin Hua Hospital, Shanghai Jiao Tong University School of Medicine, 1665 Kong Jiang Road, Shanghai, 200092, China.

Annals of Clinical Microbiology and Antimicrobials
|November 19, 2022
PubMed
Summary

A new model accurately predicts Group B Streptococcus (GBS) clonal complexes (CCs) using serotype and antibiotic resistance data. This tool aids clinical prognosis and infection control for Streptococcus agalactiae.

Keywords:
Clonal complexMachine learningMultilocus sequence typingStreptococcus agalactiae

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Area of Science:

  • Microbiology
  • Molecular Biology
  • Clinical Diagnostics

Background:

  • Group B Streptococcus (GBS) exhibits varying clinical significance across different clonal complexes (CCs).
  • Accurate GBS strain typing is crucial for clinical prognosis, epidemiological investigation, and infection control.
  • Developing a practical model for predicting GBS CCs is essential for Streptococcus agalactiae management.

Purpose of the Study:

  • To construct a practical and facile CCs prediction model for S. agalactiae.
  • To evaluate the performance of models based on antibiotic susceptibility, serotypes, and virulence genes.
  • To identify the most efficient features for accurate CCs identification.

Main Methods:

  • Collected 325 GBS strains from clinical samples.
  • Utilized multilocus sequence typing (MLST) for molecular classification and Bionumeric 8.0 software for CCs derivation.
  • Employed antibiotic susceptibility tests, multiplex PCR for serotyping, and PCR for virulence gene detection.
  • Developed multi-class CCs identification models using XGBoost algorithm based on antibiotic susceptibility (A), serotypes (S), and virulence genes (V) features.
  • Evaluated model performance using receiver operating characteristic (ROC) curves.

Main Results:

  • Identified 7 major CCs (CC1, CC10, CC12, CC17, CC19, CC23, CC24) from 325 GBS strains.
  • Found significant differences in 18 features across CCs based on A, S, and V tests.
  • The model incorporating all features (S&A&V) achieved the highest AUC of 0.9536.
  • The model using only serotype and antibiotic resistance (S&A) demonstrated strong performance (mean AUC 0.9212) with fewer parameters and no significant difference compared to the S&A&V model for most CCs.

Conclusions:

  • The S&A model offers a high accuracy and predictive power for CCs prediction with minimal parameters.
  • This established model serves as a promising tool for classifying GBS molecular types.
  • The findings suggest a substantial improvement in clinical application and epidemiological surveillance of GBS phenotyping.