Learning representations for gene ontology terms by jointly encoding graph structure and textual node descriptors

Measuring the semantic similarity between Gene Ontology (GO) terms is a fundamental step in numerous functional bioinformatics applications. To fully exploit the metadata of GO terms, word embedding-based methods have been proposed recently to map GO terms to low-dimensional feature vectors. However, these representation methods commonly overlook the key information hidden in the whole GO structure and the relationship between GO terms. In this paper, we propose a novel representation model for GO terms, named GT2Vec, which jointly considers the GO graph structure obtained by graph contrastive learning and the semantic description of GO terms based on BERT encoders. Our method is evaluated on a protein similarity task on a collection of benchmark datasets. The experimental results demonstrate the effectiveness of using a joint encoding graph structure and textual node descriptors to learn vector representations for GO terms.

Automated summary

This paper proposes a novel representation model for GO terms, named GT2Vec, which considers both the GO graph structure obtained by graph contrastive learning and the semantic description of GO terms based on BERT encoders. Experimental results demonstrate the effectiveness of the model in learning vector representations for GO terms.