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May 14, 2026Environmental Toxicology0 citationsOpen Access

Integrative Bioinformatics, Experimental Validation, and Interpretable Machine Learning Reveal Oxyresveratrol‐Mediated Protection Against Cadmium‐Induced Lung Adenocarcinoma‐Related Transcriptional Dysregulation

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MIMurat IsıyelHCHamid CeylanYDYeliz Demir

Key Points

  • The study aims to explore how oxyresveratrol protects against transcriptional dysregulation caused by cadmium.
  • Utilized integrative bioinformatics and machine learning techniques to analyze data.
  • Performed experimental validation to assess transcriptional changes.
  • Applied Elastic Net regression and SHAP analysis to identify key contributors.
  • SHAP analysis revealed Egln3 as the main contributor to transcriptional changes.
  • Cbx2 and Crabp2 were also identified as significant contributors.
  • Integrating machine learning with experimental data provided deeper insights into the mechanisms of Cd-induced dysregulation.

Abstract

= 0.90), while SHAP analysis identified Egln3 as the dominant context-dependent contributor, followed by Cbx2 and Crabp2. Complementary Elastic Net regression supported these findings through consistent linear associations. Overall, integrating interpretable machine learning with experimental evidence enhances mechanistic insight into Cd-induced transcriptional reprogramming and supports the protective role of O-RES.

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Cite This Study

Isıyel et al. (2026) studied this question.

synapsesocial.com/papers/6a0567fda550a87e60a203e6https://doi.org/10.1002/tox.70121
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