Minimum curvilinearity to enhance topological prediction of protein interactions by network embedding.

ABSTRACT:

Motivation

Most functions within the cell emerge thanks to protein-protein interactions (PPIs), yet experimental determination of PPIs is both expensive and time-consuming. PPI networks present significant levels of noise and incompleteness. Predicting interactions using only PPI-network topology (topological prediction) is difficult but essential when prior biological knowledge is absent or unreliable.

Methods

Network embedding emphasizes the relations between network proteins embedded in a low-dimensional space, in which protein pairs that are closer to each other represent good candidate interactions. To achieve network denoising, which boosts prediction performance, we first applied minimum curvilinear embedding (MCE), and then adopted shortest path (SP) in the reduced s

SUBMITTER: Cannistraci CV 

PROVIDER: S-EPMC3694668 | biostudies-literature | 2013 Jul

REPOSITORIES: biostudies-literature