Predicting whole genome protein interaction networks from primary sequence data in model and non-model organisms using ENTS.

ABSTRACT:

Background

The large-scale identification of physical protein-protein interactions (PPIs) is an important step toward understanding how biological networks evolve and generate emergent phenotypes. However, experimental identification of PPIs is a laborious and error-prone process, and current methods of PPI prediction tend to be highly conservative or require large amounts of functional data that may not be available for newly-sequenced organisms.

Results

In this study we demonstrate a random-forest based technique, ENTS, for the computational prediction of protein-protein interactions based only on primary sequence data. Our approach is able to efficiently predict interactions on a whole-genome scale for any eukaryotic organism, using pairwise combinations of conserved doma

SUBMITTER: Rodgers-Melnick E 

PROVIDER: S-EPMC3848842 | biostudies-literature | 2013 Sep

REPOSITORIES: biostudies-literature