%0 Journal Article
%T Features analysis for identification of date and party hubs in protein interaction network of Saccharomyces Cerevisiae
%A Mitra Mirzarezaee
%A Babak N Araabi
%A Mehdi Sadeghi
%J BMC Systems Biology
%D 2010
%I BioMed Central
%R 10.1186/1752-0509-4-172
%X We extracted different biological characteristics including amino acid sequences, domain contents, repeated domains, functional categories, biological processes, cellular compartments, disordered regions, and position specific scoring matrix from various sources. Several classifiers are examined and the best feature-sets based on average correct classification rate and correlation coefficients of the results are selected. We show that fusion of five feature-sets including domains, Position Specific Scoring Matrix-400, cellular compartments level one, and composition pairs with two and one gaps provide the best discrimination with an average correct classification rate of 77%.We study a variety of known biological feature-sets of the proteins and show that there is a relation between domains, Position Specific Scoring Matrix-400, cellular compartments level one, composition pairs with two and one gaps of Saccharomyces Cerevisiae's proteins, and their roles in the protein interaction network as non-hubs, intermediately connected, party hubs and date hubs. This study also confirms the possibility of predicting non-hubs, party hubs and date hubs based on their biological features with acceptable accuracy. If such a hypothesis is correct for other species as well, similar methods can be applied to predict the roles of proteins in those species.Proteins are important components of all living organisms. They are responsible for essential functions within cells. Most proteins perform their biological functions through interacting with other proteins [1]. Map of the whole physical protein interactions inside an organism forms a network called Protein Interaction Network (PIN). Although large-scale PINs have already been determined experimentally for several species; in general there is a lack of protein interaction data for many species, and the computational prediction of protein interactions are still among the most wanted solutions in protein bioinformatics [2]. These net
%U http://www.biomedcentral.com/1752-0509/4/172