Application of machine learning algorithms to screen potential biomarkers under cadmium exposure based on human urine metabolic profiles

Ting Zeng, Yanshan Liang, Qingyuan Dai, Jinglin Tian, Jinyao Chen, Bo Lei, Zhu Yang, Zongwei Cai*

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

16 Citations (Scopus)

Abstract

Exposure to environmental cadmium increases the health risk of residents. Early urine metabolic detection using high-resolution mass spectrometry and machine learning algorithms would be advantageous to predict the adverse health effects. Here, we conducted machine learning approaches to screen potential biomarkers under cadmium exposure in 403 urine samples. In positive and negative ionization mode, 4207 and 3558 features were extracted, respectively. We compared seven machine learning algorithms and found that the extreme gradient boosting (XGBoost) and random forest (RF) classifiers showed better accuracy and predictive performance than others. Following 5-fold cross-validation, the value of area under curve (AUC) was both 0.93 for positive and negative ionization modes in XGBoost classifier. In the RF classifier, AUC were 0.80 and 0.84 for positive and negative ionization modes, respectively. We then identified a biomarker panel based on XGBoost and RF classifiers. The incorporation of machine learning models into urine analysis using high-resolution mass spectrometry could allow a convenient assessment of cadmium exposure.

Original languageEnglish
Pages (from-to)5184-5188
Number of pages5
JournalChinese Chemical Letters
Volume33
Issue number12
Early online date7 Mar 2022
DOIs
Publication statusPublished - Dec 2022

Scopus Subject Areas

  • Chemistry(all)

User-Defined Keywords

  • Cadmium exposure
  • High-resolution mass spectrometry
  • Human urine
  • Machine learning
  • Metabolic profiles

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