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

In this study, we propose a novel GO term representation learning method, PO2Vec, to utilize the partial order relationships to improve the GO term representations. Extensive evaluations show that PO2Vec achieves better outcomes than existing embedding methods in a variety of downstream biological tasks. Based on PO2Vec, we further developed a new protein function prediction method PO2GO, which demonstrates superior performance measured in multiple metrics and annotation specificity as well as few-shot prediction capability in the benchmarks. These results suggest that the high-quality representation of GO structure is critical for diverse biological tasks including computational protein annotation.PMID:38446740 | PMC:PMC10917077 | DOI:10.1093/bib/bbae077

Source: Briefings in Bioinformatics - March 6, 2024 Category: Bioinformatics Authors: Wenjing Li Bin Wang Jin Dai Yan Kou Xiaojun Chen Yi Pan Shuangwei Hu Zhenjiang Zech Xu Source Type: research