Prediction of Adverse Biological Effects of Chemicals Using Knowledge Graph Embeddings

• URL: http://arxiv.org/abs/2112.04605v1
• Date: Wed, 8 Dec 2021 22:19:16 GMT
• Title: Prediction of Adverse Biological Effects of Chemicals Using Knowledge Graph Embeddings
• Authors: Erik B. Myklebust, Ernesto Jim\'enez-Ruiz, Jiaoyan Chen, Raoul Wolf, Knut Erik Tollefsen
• Abstract summary: We show that using knowledge graph embeddings can increase the accuracy of effect prediction with neural networks. We evaluate certain characteristics of the knowledge graph embedding models to shed light on the individual model performance.
• Score: 5.1168938454615205
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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