ArticleChemical research in toxicology2026
Interpretable Machine Learning to Understand Wildfire Toxicity: Bridging Chemicals, Omics, and Toxicological Outcomes via Symbolic Regression with Novel Feature Scoring.
Article in Chemical research in toxicology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Wildfire smoke exposures are increasingly common, consisting of complex mixtures of gases and particulates known to cause diverse pulmonary health effects. While health outcomes are regularly studied, quantitative links between smoke chemical composition and toxicological outcomes remain poorly defined, limiting interpretation of wildfire smoke health risks. This study explores symbolic regression (SR) as an interpretable artificial intelligence/machine learning method to generate closed-form mathematical models linking chemical exposure to biological responses relevant to wildfire smoke. Prior to application on wildfire-relevant data sets, we benchmarked three Python-based SR packages on simulated data, assessing performance across varying noise levels and operator complexities. Insights from these simulation tests, such as the importance of including necessary operators, were incorporated when applying SR to lab-generated wildland fire exposure-toxicity data. This data set included chemical characterizations of biomass smoke exposures and corresponding pulmonary responses in female CD-1 mice (
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