ProCbA: Protein Function Prediction based on Clique Analysis

Abstract

Protein function prediction based on protein-protein interactions (PPI) is one of the most important challenges of the post-Genomic era. Due to the fact that determining protein function by experimental techniques can be costly, function prediction has become an important challenge for computational biology and bioinformatics. Some researchers utilize graph- (or network-) based methods using PPI networks for unannotated proteins. The aim of this study is to increase the accuracy of the protein function prediction using two proposed methods. To predict protein functions, we propose a Protein Function Prediction based on Clique Analysis (ProCbA) and Protein Function Prediction on Neighborhood Counting using functional aggregation (ProNC-FA). Both ProCbA and ProNC-FA can predict the functions of unknown proteins. In addition, in ProNC-FA which does not include a new algorithm; we attempt to solve the essence of incomplete and noisy data of the PPI era in order to achieve a network with complete functional aggregation. The experimental results on MIPS data and the 17 different explained datasets validate the encouraging performance and the strength of both ProCbA and ProNC-FA on function prediction. Experimental result analysis demonstrates that both ProCbA and ProNC-FA are generally able to outperform all the other methods.