Graph-based prediction of Protein-protein interactions with attributed signed graph embedding.

ABSTRACT:

Background

Protein-protein interactions (PPIs) are central to many biological processes. Considering that the experimental methods for identifying PPIs are time-consuming and expensive, it is important to develop automated computational methods to better predict PPIs. Various machine learning methods have been proposed, including a deep learning technique which is sequence-based that has achieved promising results. However, it only focuses on sequence information while ignoring the structural information of PPI networks. Structural information of PPI networks such as their degree, position, and neighboring nodes in a graph has been proved to be informative in PPI prediction.

Results

Facing the challenge of representing graph information, we introduce an improved graph repres

SUBMITTER: Yang F 

PROVIDER: S-EPMC7372763 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature