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Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization

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Published on: July 27, 2021

Towards systematic read-across using Generalised Read-Across (GenRA).

Grace Patlewicz¹, Imran Shah¹

¹Center for Computational Toxicology and Exposure (CCTE), Office of Research and Development, US Environmental Protection Agency, 109 TW Alexander Dr, Research Triangle Park, NC 27711, USA.

Computational Toxicology (Amsterdam, Netherlands)

|September 11, 2023

Summary

This summary is machine-generated.

The Generalised Read-Across (GenRA) approach offers an algorithmic solution to quantify performance and uncertainty in read-across assessments. This method aims to standardize and objectively evaluate data gap filling techniques in chemical safety.

Keywords:

GenRA New Approach Methods (NAMs)Read-Across ToxCast

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Area of Science:

Chemical safety assessment
Toxicology
Computational toxicology

Background:

Read-across is a key data gap filling method in chemical safety.
Uncertainty quantification remains a challenge for expert-driven read-across.
Existing frameworks lack objective performance evaluation and uncertainty metrics.

Purpose of the Study:

Introduce the Generalised Read-Across (GenRA) algorithmic approach.
Quantify performance and uncertainty in read-across assessments.
Provide an objective means for evaluating read-across predictions.

Main Methods:

Development of the GenRA algorithmic framework.
Quantification of similarity context impacts in read-across.
Creation of software tools for GenRA implementation.

Main Results:

Demonstration of GenRA's capability to quantify performance and uncertainty.
Illustration of GenRA's application in screening-level hazard assessments.
Progress in standardizing the evaluation of similarity contexts.

Conclusions:

GenRA provides an objective, algorithmic approach to read-across.
The approach facilitates quantification of performance and uncertainty.
GenRA can support more consistent and reliable chemical safety decisions.