Prediction of heterodimeric protein complexes from weighted protein-protein interaction networks using novel features and kernel functions.

ABSTRACT: Since many proteins express their functional activity by interacting with other proteins and forming protein complexes, it is very useful to identify sets of proteins that form complexes. For that purpose, many prediction methods for protein complexes from protein-protein interactions have been developed such as MCL, MCODE, RNSC, PCP, RRW, and NWE. These methods have dealt with only complexes with size of more than three because the methods often are based on some density of subgraphs. However, heterodimeric protein complexes that consist of two distinct proteins occupy a large part according to several comprehensive databases of known complexes. In this paper, we propose several feature space mappings from protein-protein interaction data, in which each interaction is weighted based on re

SUBMITTER: Ruan P 

PROVIDER: S-EPMC3679142 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature