Biased random walk with restart for essential proteins prediction

Abstract

Predicting essential proteins is crucial for discovering the process of cellular organization and viability. We propose biased random walk with restart algorithm for essential proteins prediction, called BRWR. Firstly, the common process of practice walk often sets the probability of particles transferring to adjacent nodes to be equal, neglecting the influence of the similarity structure on the transition probability. To address this problem, we redefine a novel transition probability matrix by integrating the gene express similarity and subcellular location similarity. The particles can obtain biased transferring probabilities to perform random walk so as to further exploit biological properties embedded in the network structure. Secondly, we use gene ontology (GO) terms score and subcellular score to calculate the initial probability vector of the random walk with restart. Finally, when the biased random walk with restart process reaches steady state, the protein importance score is obtained. In order to demonstrate superiority of BRWR, we conduct experiments on the YHQ, BioGRID, Krogan and Gavin PPI networks. The results show that the method BRWR is superior to other state-of-the-art methods in essential proteins recognition performance. Especially, compared with the contrast methods, the improvements of BRWR in terms of the ACC results range in 1.4%–5.7%, 1.3%–11.9%, 2.4%–8.8%, and 0.8%–14.2%, respectively. Therefore, BRWR is effective and reasonable.