Prediction of Adverse Biological Effects of Chemicals Using Knowledge Graph Embeddings
This work addresses chemical risk assessment for ecotoxicologists, presenting an incremental improvement through fine-tuning of existing embedding methods.
The paper tackled chemical effect prediction for ecotoxicological risk assessment by applying knowledge graph embeddings, showing that they increase prediction accuracy with neural networks and that a fine-tuning architecture further improves performance.
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 that adapts the knowledge graph embeddings to the effect prediction task and leads to better performance. Finally, we evaluate certain characteristics of the knowledge graph embedding models to shed light on the individual model performance.