Partial order relation-based gene ontology embedding improves protein function prediction.

ABSTRACT: Protein annotation has long been a challenging task in computational biology. Gene Ontology (GO) has become one of the most popular frameworks to describe protein functions and their relationships. Prediction of a protein annotation with proper GO terms demands high-quality GO term representation learning, which aims to learn a low-dimensional dense vector representation with accompanying semantic meaning for each functional label, also known as embedding. However, existing GO term embedding methods, which mainly take into account ancestral co-occurrence information, have yet to capture the full topological information in the GO-directed acyclic graph (DAG). In this study, we propose a novel GO term representation learning method, PO2Vec, to utilize the partial order relationships to impro

SUBMITTER: Li W 

PROVIDER: S-EPMC10917077 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature