From Target Prediction to Mechanistic Insights: Revealing Air Pollution-Driven Mechanisms in Endometrial Cancer via Interpretable Machine Learning and Molecular Docking

From Target Prediction to Mechanistic Insights: Revealing Air Pollution-Driven Mechanisms in Endometrial Cancer via Interpretable Machine Learning and Molecular Docking

Air pollution is a known contributor to cancer risk, although its specific impact on endometrial cancer (EC) remains unclear. This study integrates network toxicology, transcriptomics, molecular docking, and machine learning to investigate pollutant–gene interactions in EC. We identify 83 air pollut...

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Bibliographic Details
Main Authors: Hongyao Liu, Yueqing Zou
Format: Article
Language:English
Published: MDPI AG 2025-07-01
Series:Atmosphere
Subjects:
air pollutant
endometrial cancer
SHAP
molecular docking
machine learning
Online Access:https://www.mdpi.com/2073-4433/16/7/841
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https://www.mdpi.com/2073-4433/16/7/841

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