acePredicting Hormesis Effects of GenX in Zebrafish via Interpretable Machine Learning: Insights From SHAP Analysis
Hexafluoropropylene oxide-dimer acid (GenX), a prominent alternative to legacy per- and polyfluoroalkyl substances (PFAS), poses a significant challenge to traditional linear risk assessment models due to its ability to induce hormesis-a biphasic "low-dose stimulation, high-dose inhibition" response. This study established an interpretable machine learning (ML) framework to identify and predict GenX-induced non-monotonic dose-response (NMDR) relationships in zebrafish (Danio rerio). By integrating 263 independen…
Interpretable CatBoost-based machine learning framework predicted GenX-induced biphasic dose-responses in zebrafish with high fidelity, enabling mechanistic ecological risk assessment of PFAS alternatives.
Evidence
- Peer-reviewedIUBMB Life2026-10-01
How should this claim be treated?
Truvace Impact Record TRV-2026-1242, v1: “Predicting Hormesis Effects of GenX in Zebrafish via Interpretable Machine Learning: Insights From SHAP Analysis.” Truvace, 2026-10-02. /record/TRV-2026-1242 (accessed at citation time). sha256 e05c56472cbc3eca…
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