Calibrated Chemical-Agnostic qAOPs for Nonmutagenic Liver Tumor Risk Assessment

A 5.1 KB ZIP file published by Zheng Zhou on figshare in April 2026. The dataset contains results from a method to calibrate Quantitative Adverse Outcome Pathways (qAOPs) on multiple chemical data, aiming to separate chemical-specific heterogeneity from core pathway effects. It includes a simulation study and a case study applying the calibration to derive points of departure for a nonmutagenic liver tumor qAOP.

Use Cases

• Training hierarchical models to separate chemical-specific heterogeneity from pathway-level effects based on the described calibration approach.
• Benchmarking qAOP model performance with and without hierarchical calibration to assess data heterogeneity.
• Deriving points of departure for chemical risk assessment based on the nonmutagenic liver tumor case study.

Strengths

• Dataset is 5.1 KB, facilitating quick download and inspection.
• The method explicitly models chemical-specific deviations as random effects to extract core pathway parameters.
• Includes a demonstrated case study on a nonmutagenic liver tumor qAOP.

Limitations

• Row count and column-level documentation are absent; field semantics must be inferred after download.
• The dataset's small size (5.1 KB) suggests it contains model outputs or parameters, not raw experimental data.

Provenance

Source

figshare

Collection Method

Developed through a simulation study and applied to a case study, as described in the abstract.

Time Range

Publication date is 2026-04-14.

Freshness

Last updated 2026-04-14 13:38:28.

Geography

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