Random walk based method to identify essential proteins by integrating network topology and biological characteristics.

Essential proteins are regarded as the fundamental components of living organisms. The identification of essential proteins greatly contributes to the understanding of cellular functions and biological mechanisms. There are a variety of experimental as well as computational methods which have been used for essential protein detection. However, it is still a big challenge to further improve the precision of essential proteins prediction. In this paper, we introduce a novel essential proteins exploration method named RWEP, which adopts random walk algorithm and integrates the topological and biological properties to determine protein essentiality in protein–protein interaction (PPI) networks. In this method, first, PPIs are weighted based on topology of networks, gene expression and GO annotation data. Then each protein in a PPI network is assigned an initial score by exploiting subcellular localization and protein complexes information. Finally, we apply a random walk with restart (RWR) algorithm on the weighted PPI networks to iteratively score proteins. To demonstrate the performance of RWEP, we have carried out a series of experiments on four different yeast datasets (DIP, MIPS, Krogan and Gavin). The computational experiments confirm the efficiency of RWEP in predicting essential proteins. Compared with other state-of-the-art essential proteins identification methods, RWEP achieves a superior performance in terms of various evaluation criteria.