Chemical-Agnostic Quantitative Adverse Outcome Pathways with Hierarchical Calibration

A 36.6 KB Excel dataset presenting a calibration method for Quantitative Adverse Outcome Pathways (qAOPs) to separate chemical-specific heterogeneity from core pathway effects. The dataset, authored by Zheng Zhou and last updated in April 2026, includes a simulation study and a case study on nonmutagenic liver tumor qAOPs to demonstrate the approach.

Use Cases

• Calibrating quantitative adverse outcome pathways (qAOPs) based on the described hierarchical modeling approach.
• Deriving points of departure for chemical risk assessment based on the case study methodology.
• Analyzing cross-chemical heterogeneity in toxicological data based on the described simulation study.
• Evaluating model reliability and generalizability for chemical-agnostic qAOPs based on the described performance metrics.

Strengths

• Dataset is 36.6 KB, facilitating quick download and inspection.
• Includes a demonstrated case study on a nonmutagenic liver tumor qAOP.
• Methodology is explicitly described for separating chemical-specific heterogeneity from pathway effects.

Limitations

• Row count is unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• Description metadata is limited; actual data quality requires manual inspection after download.

Provenance

Source

figshare

Collection Method

Likely contains results from a simulation study and a case study on qAOP calibration.

Time Range

Dataset last updated in April 2026; temporal coverage of the underlying data is unspecified.

Freshness

Last updated 2026-04-14 13:38:27; freshness should be verified.

Geography

Spatial coverage is not specified in the description.