Prediction of adverse biological effects of chemicals using knowledge graph embeddings

Semantic web

article

Erik B. Myklebust¹  Ernesto Jiménez-Ruiz²  Jiaoyan Chen⁵  Raoul Wolf¹  Knut Erik Tollefsen¹ 

[1]Norwegian Institute for Water Research

[2]SIRIUS, University of Oslo

[3]NORSAR

[4]City, University of London

[5]University of Oxford

[6]Norwegian Geotechnical Institute

[7]Norwegian University of Life Sciences

关键词: Knowledge graph;    ecotoxicology;    risk assessment;    adverse effects;    embedding;    chemicals;    species;   

DOI  :  10.3233/SW-222804

来源: IOS Press

【 摘 要 】

We have created a knowledge graph based on major data sources used in ecotoxicological risk assessment. We have applied this knowledge graph to an important task in risk assessment, namely chemical effect prediction. We have evaluated nine knowledge graph embedding models from a selection of geometric, decomposition, and convolutional models on this prediction task. We show that using knowledge graph embeddings can increase the accuracy of effect prediction with neural networks. Furthermore, we have implemented a fine-tuning architecture which adapts the knowledge graph embeddings to the effect prediction task and leads to a better performance. Finally, we evaluate certain characteristics of the knowledge graph embedding models to shed light on the individual model performance.

【 授权许可】

Unknown