GORetriever: reranking protein-description-based GO candidates by literature-driven deep information retrieval for protein function annotation.

ABSTRACT:

Summary

The vast majority of proteins still lack experimentally validated functional annotations, which highlights the importance of developing high-performance automated protein function prediction/annotation (AFP) methods. While existing approaches focus on protein sequences, networks, and structural data, textual information related to proteins has been overlooked. However, roughly 82% of SwissProt proteins already possess literature information that experts have annotated. To efficiently and effectively use literature information, we present GORetriever, a two-stage deep information retrieval-based method for AFP. Given a target protein, in the first stage, candidate Gene Ontology (GO) terms are retrieved by using annotated proteins with similar descriptions. In the second stag

SUBMITTER: Yan H 

PROVIDER: S-EPMC11520413 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature