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Related Experiment Video

Updated: Apr 25, 2026

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
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From Data to Mechanism: A Knowledge Graph-Bayesian Network-Driven Suspect Screening (KGBS) Strategy for Phthalate

Dian Wang1,2, Fei Cheng1, Juntao Cui1

  • 1State Key Laboratory of Advanced Environmental Technology, Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou 510640, China.

Environmental Science & Technology
|April 24, 2026
PubMed
Summary

A new strategy using knowledge graphs and Bayesian networks identifies phthalate esters (PAEs) and their metabolites in humans. This approach aids in understanding environmental exposure and human health risks.

Keywords:
Bayesian inferencebig data suspect screeninghuman urineindoor dustknowledge graphsphthalate estersphthalate metabolitestext mining

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Area of Science:

  • Environmental Chemistry
  • Metabolomics
  • Toxicology

Background:

  • Phthalate esters (PAEs) are widely used, leading to complex human exposure and metabolic transformations.
  • Understanding these biotransformation pathways is crucial for assessing human health risks.

Purpose of the Study:

  • To develop a systematic strategy for elucidating PAE biotransformation relationships.
  • To identify and characterize PAEs and their metabolites in human samples.

Main Methods:

  • Developed a knowledge graph-Bayesian network-driven suspect screening (KGBS) strategy.
  • Integrated text mining, probabilistic reasoning, and high-resolution mass spectrometry (HRMS).
  • Constructed a Bayesian inference-embedded knowledge graph from 3167 publications, extracting 54,838 PAE-metabolite associations.

Main Results:

  • Applied the KGBS framework to indoor dust and human urine samples.
  • Identified 68 PAEs and 49 metabolites, with 18 PAEs and 14 metabolites newly annotated.
  • Prioritization scores and MS2 fragmentation fingerprints supported metabolic linkages.

Conclusions:

  • The KGBS strategy enables pathway-centric interpretation of contaminants and their transformation products.
  • Established a scalable, self-evolving strategy for reconstructing biotransformation networks.
  • Offers new perspectives for exposome characterization and human exposure risk assessment.