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Related Concept Videos

Candidiasis01:20

Candidiasis

Candidiasis is a fungal infection caused by opportunistic species of Candida. It can affect various anatomical sites, including the skin, oral cavity, nails, and genitourinary tract. Among its forms, vaginal candidiasis is the most common type of mucosal infection. It typically results from the overgrowth of Candida albicans in the vaginal mucosa. Under normal conditions, C. albicans exists as a commensal organism within the vaginal microbiota, regulated by the dominance of lactobacilli, which...
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Related Experiment Video

Updated: Jul 5, 2026

Whole Genome Sequencing of Candida glabrata for Detection of Markers of Antifungal Drug Resistance
08:45

Whole Genome Sequencing of Candida glabrata for Detection of Markers of Antifungal Drug Resistance

Published on: December 28, 2017

Machine Learning-Based Drug Susceptibility Prediction from Candida Genomic Data.

Zhaohui Wei1, Shuguang Li1, Shuyi Wang2

  • 1Department of Clinical Laboratory, Peking University People's Hospital, Beijing, 100044, China; Institute of Medical Technology, Peking University Health Science Center, Beijing, 100191, China; Beijing Key Laboratory of Innovative & Transformable Warning and Intervention Technologies for Drug-Resistant Pathogens, Beijing, 100871, China.

International Journal of Antimicrobial Agents
|July 3, 2026
PubMed
Summary

Rising antifungal resistance in invasive Candida infections is a major concern. Whole-genome sequencing combined with machine learning accurately predicts antifungal susceptibility, aiding earlier treatment.

Keywords:
Antifungal susceptibility testingCandidaMachine learningMinimum inhibitory concentration predictionWhole-genome sequencing

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Area of Science:

  • Medical Mycology
  • Genomics
  • Computational Biology

Background:

  • Invasive Candida infections pose a growing clinical challenge due to increasing antifungal resistance.
  • Current antifungal susceptibility testing (AFST) methods lack the speed and accuracy needed for routine clinical practice.

Purpose of the Study:

  • To evaluate the species distribution and antifungal susceptibility of invasive Candida isolates in China.
  • To assess the feasibility of using whole-genome sequencing (WGS) and machine learning (ML) to predict minimum inhibitory concentrations (MICs).

Main Methods:

  • Collected 337 invasive Candida isolates from 20 hospitals in China (2022-2023).
  • Determined MICs for nine antifungal agents using broth microdilution.
  • Performed WGS on prevalent species and utilized genomic 11-mer features to train and optimize Random Forest (RF), Support Vector Machine (SVM), and Extreme Gradient Boosting (XGBoost) models.

Main Results:

  • Identified dominant species: C. albicans (n=103), C. tropicalis (n=71), C. parapsilosis (n=67), and C. glabrata (n=63).
  • Observed higher azole and echinocandin resistance in non-albicans Candida species, with notable resistance in C. tropicalis (azoles) and C. glabrata (echinocandins).
  • The optimized RF model achieved high accuracy (AUC 0.979), with essential agreement >90.1% and categorical agreement >93.2% for MIC prediction.

Conclusions:

  • Non-albicans Candida species present a significant clinical challenge due to emerging antifungal resistance.
  • WGS combined with ML offers a highly accurate, potentially rapid method for predicting antifungal susceptibility, supporting earlier and more effective antifungal therapy.