Comparative Analysis of Unsupervised Protein Similarity Prediction Based on Graph Embedding.

ABSTRACT: The study of protein-protein interaction and the determination of protein functions are important parts of proteomics. Computational methods are used to study the similarity between proteins based on Gene Ontology (GO) to explore their functions and possible interactions. GO is a series of standardized terms that describe gene products from molecular functions, biological processes, and cell components. Previous studies on assessing the similarity of GO terms were primarily based on Information Content (IC) between GO terms to measure the similarity of proteins. However, these methods tend to ignore the structural information between GO terms. Therefore, considering the structural information of GO terms, we systematically analyze the performance of the GO graph and GO Annotation (GOA) gra

SUBMITTER: Zhang Y 

PROVIDER: S-EPMC8493040 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature