Graph2GO: a multi-modal attributed network embedding method for inferring protein functions.

ABSTRACT:

Background

Identifying protein functions is important for many biological applications. Since experimental functional characterization of proteins is time-consuming and costly, accurate and efficient computational methods for predicting protein functions are in great demand for generating the testable hypotheses guiding large-scale experiments."

Results

Here, we propose Graph2GO, a multi-modal graph-based representation learning model that can integrate heterogeneous information, including multiple types of interaction networks (sequence similarity network and protein-protein interaction network) and protein features (amino acid sequence, subcellular location, and protein domains) to predict protein functions on gene ontology. Comparing Graph2GO to BLAST, as a baseline model

SUBMITTER: Fan K 

PROVIDER: S-EPMC7414417 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature