Identification of Protein Subcellular Localization With Network and Functional Embeddings.

ABSTRACT: The functions of proteins are mainly determined by their subcellular localizations in cells. Currently, many computational methods for predicting the subcellular localization of proteins have been proposed. However, these methods require further improvement, especially when used in protein representations. In this study, we present an embedding-based method for predicting the subcellular localization of proteins. We first learn the functional embeddings of KEGG/GO terms, which are further used in representing proteins. Then, we characterize the network embeddings of proteins on a protein-protein network. The functional and network embeddings are combined as novel representations of protein locations for the construction of the final classification model. In our collected benchmark dataset

SUBMITTER: Pan X 

PROVIDER: S-EPMC7873866 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature