Understanding Adverse Biological Effect Predictions Using Knowledge Graphs

TL;DR

This paper demonstrates how knowledge graphs can enhance the prediction of adverse biological effects of chemicals, significantly improving model accuracy and providing insights to support hazard assessment without animal testing.

Contribution

It introduces a knowledge graph-based approach to predict biological effects of chemicals, achieving up to 40% improvement in prediction performance over models without background knowledge.

Findings

Knowledge graphs improve prediction accuracy by up to 40%.

KG embeddings offer qualitative insights into predictions.

The approach supports hazard assessment with reduced testing.

Abstract

Extrapolation of adverse biological (toxic) effects of chemicals is an important contribution to expand available hazard data in (eco)toxicology without the use of animals in laboratory experiments. In this work, we extrapolate effects based on a knowledge graph (KG) consisting of the most relevant effect data as domain-specific background knowledge. An effect prediction model, with and without background knowledge, was used to predict mean adverse biological effect concentration of chemicals as a prototypical type of stressors. The background knowledge improves the model prediction performance by up to 40\% in terms of $R^2$ (\ie coefficient of determination). We use the KG and KG embeddings to provide quantitative and qualitative insights into the predictions. These insights are expected to improve the confidence in effect prediction. Larger scale implementation of such extrapolation models should be expected to support hazard and risk assessment, by simplifying and reducing testing needs.