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Predicting 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…

By the Truvace desk1 min read1 sourcePeer-reviewedTRV-2026-1242· cite & verify
Predicting Hormesis Effects of GenX in Zebrafish via Interpretable Machine Learning: Insights From SHAP Analysis
Zebrafish Make a Splash in FDA Research (8614532586) by The U.S. Food and Drug Administration. Public domain

In brief

On 2026-10-01, a peer-reviewed study described an interpretable machine learning framework to identify and predict non-monotonic dose-response relationships for GenX, a PFAS alternative, in zebrafish. Using 263 records across 11 biological categories, the CatBoost-based Master Model reached AUC 0.977 and 91.25% accuracy, with SHAP analysis highlighting effect measurement and biological category as key drivers.

The work matters because it shifts ecological risk assessment from empirical curve-fitting to mechanistic predictive generalization for emerging PFAS alternatives, identifying immune, endocrine, reproductive and neurobehavioral endpoints as most sensitive. What remains uncertain is how well predictions transfer beyond zebrafish laboratory data to field ecosystems and human-relevant exposures.

Main points

  1. Study integrated 263 independent experimental records across 11 biological categories to model non-monotonic dose-response relationships.
  2. Benchmarked 6 ML paradigms; CatBoost-based Master Model achieved AUC 0.977 and 91.25% accuracy.
  3. SHAP analysis identified Effect Measurement and Biological Category as primary determinants, with immune, endocrine, reproductive and neurobehavioral endpoints most sensitive to low-dose stimulation.
  4. SAR analysis flagged MolLogP, SlogP_VSA3, VSA_EState series and Chi1v as core structural drivers linked to oxidative stress signaling and metabolic trade-off.

The gain

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.

The rundown

Researchers compiled 263 zebrafish experimental records spanning 11 biological categories to address GenX-induced hormesis, defined as low-dose stimulation, high-dose inhibition that challenges linear risk models.

The CatBoost-based Master Model outperformed five other ML paradigms, and SHAP interpretation plus SAR analysis linked hormetic sensitivity to specific systems and to lipophilicity, electrostatic and topological descriptors like MolLogP and Chi1v.

Sources

  1. Peer-reviewedIUBMB Life2026-10-01

The debate