The essential protein detection on protein–protein interaction (PPI) network can not only promote the research of life science, but also have important applications in disease diagnosis and drug target cell identifying. A large number of computation-based essential protein detection algorithms have been presented recently. Most of those methods detect the essential proteins according to the centrality measures of the nodes in PPI networks. Those centrality-based essential protein detection methods only consider the topological information of the PPI networks and neglect the biological features of the proteins which are crucial in recognizing the essential proteins. This paper presents a random walk-based method named EPD-RW to identify essential proteins by integrating network topology and biological information extracted from GO (gene ontology) data, gene expression profiles, domain information and phylogenetic profile. EPD-RW uses both the topological structure of the PPI and biological information of the proteins to guide the random walk for computing their essentialness. An iterative method is presented to efficiently integrate the topological and biological features at each step of the random walk. We test our method EDP-RW by experiments on yeast PPI datasets. We also compare the test results of EDP-RW with those of other methods. The experimental results demonstrate that EPD-RW can achieve the best performance among all the methods tested. The biological illustration of the results shows that our random walk-based method effectively increases the accuracy of essential proteins detecting results, and the biological features of the proteins can greatly enhance the performance of essential protein detecting.

蛋白质-蛋白质相互作用（PPI）网络上必需的蛋白质检测不仅可以促进生命科学的研究，而且在疾病诊断和药物靶细胞鉴定中具有重要的应用。最近已经提出了许多基于计算的必需蛋白质检测算法。这些方法中的大多数根据PPI网络中节点的中心性度量来检测必需蛋白。这些基于中心性的必需蛋白检测方法仅考虑了PPI网络的拓扑信息，而忽略了对识别必需蛋白至关重要的蛋白生物学特性。本文提出了一种基于随机行走的方法，称为EPD-RW，它通过整合网络拓扑和从GO（基因本体）数据中提取的生物学信息，基因表达谱，结构域信息和系统发育谱来鉴定必需蛋白。EPD-RW使用PPI的拓扑结构和蛋白质的生物学信息来指导随机游走，以计算其必要性。提出了一种迭代方法，可以有效地整合随机游走的每个步骤中的拓扑和生物学特征。我们通过对酵母PPI数据集进行实验来测试EDP-RW方法。我们还将EDP-RW的测试结果与其他方法进行了比较。实验结果表明，EPD-RW可以在所有测试方法中实现最佳性能。