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Predicting missing annotations in Gene Ontology with Knowledge Graph Embeddings and True Path Rule
Özge Erten, Shervin Mehryar, Remzi Celebi, Christopher Brewster
Gene Ontology (GO) and its Annotations (GOA) provide a controlled and evolving vocabulary for gene products and gene functions widely used in molecular biology. GO \& GOA are updated and maintained both automatically from biological publications and manually by curators. These knowledge bases however are often incomplete for two reasons: 1) Research in biological domain itself is still ongoing; 2) The amount of experimental evidence might not be yet sufficient to validate annotations. In this paper, we address the gap in evidence between gene products and their annotations by making link predictions using Knowledge Graph Embedding (KGE) methods. Through the application of the True Path Rule (TPR) in the training stage of KGE, we were able to improve the performance of traditional KGE methods. We report two experimental scenarios with GO and GO Chicken Annotation datasets to show the contribution of embedding TPR to prediction accuracy.
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