Essential proteins are vital for cellular survival and development. Identifying essential proteins is very important for helping us understand the way in which a cell works. Rapid increase of available protein-protein interaction (PPI) data has made it possible to detect protein essentiality at the network level. A series of centrality measures have been proposed to discover essential proteins based on the PPI networks. However, the PPI data obtained from large scale, high-throughput experiments generally contain false positives. It is insufficient to use original PPI data to identify essential proteins. In this paper, we firstly adopt a dynamic model-based method to filter noisy data from time-course gene expression profiles. Second, a threshold of each protein is calculated from a threshold function of σ, the protein is active at a time point if its expression level is higher than the threshold. Two proteins are regarded as co-expression if they are all active at the same time point. Finally, an active PPI network is constructed by combining gene expression data with PPI data.
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