Improving protein function prediction by learning and integrating representations of protein sequences and function labels.

ABSTRACT: As fewer than 1% of proteins have protein function information determined experimentally, computationally predicting the function of proteins is critical for obtaining functional information for most proteins and has been a major challenge in protein bioinformatics. Despite the significant progress made in protein function prediction by the community in the last decade, the general accuracy of protein function prediction is still not high, particularly for rare function terms associated with few proteins in the protein function annotation database such as the UniProt. We introduce TransFew, a new transformer model, to learn the representations of both protein sequences and function labels [Gene Ontology (GO) terms] to predict the function of proteins. TransFew leverages a large pre-trained

SUBMITTER: Boadu F 

PROVIDER: S-EPMC11374024 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature