A New Scheme for Essential Protein Identification Based on Uncertain Networks

Abstract

Identifying essential proteins is important for not only understanding cellular activity but also detecting human disease genes. A series of centrality measures have been proposed to identify essential proteins based on the protein-protein interaction (PPI) network. Although, existing studies have focused on the topological features of the PPI network and the intrinsic characteristics of biological attributes. it is still a big challenge to further improve the prediction accuracy of essential proteins. Moreover, there are substantial amounts of false-positive data in PPI networks; thus, a PPI network should be modelled as an uncertain network. How to identify essential proteins more accurately and conveniently has become a research hotspot. In this paper, we proposed a new essential protein discovery method called ETB-UPPI on uncertain PPI networks. The algorithm detects essential proteins by integrating topological features with biological information. Experimental results on four Saccharomyces cerevisiae datasets have shown that ETB-UPPI can not only improve the prediction accuracy but also outperform other prediction methods, including the most commonly-used centrality measures (DC, SC, BC, IC, EC, and NC), topology-based methods (LAC) and biological-data-integrating methods (PeC, WDC, UDONC, LBCC, TEGS, and RSG).