  Essential protein plays a crucial role in the process of cell life. The
identification of essential proteins can not only promote the development of
drug target technology, but also contribute to the mechanism of biological
evolution. There are plenty of scholars who pay attention to discovering
essential proteins according to the topological structure of protein network
and biological information. The accuracy of protein recognition still demands
to be improved. In this paper, we propose a method which integrate the
clustering coefficient in protein complexes and topological properties to
determine the essentiality of proteins. First, we give the definition of
In-clustering coefficient (IC) to describe the properties of protein complexes.
Then we propose a new method, complex edge and node clustering coefficient
(CENC) to identify essential proteins. Different Protein-Protein Interaction
(PPI) networks of Saccharomyces cerevisiae, MIPS and DIP are used as
experimental materials. Through some experiments of logistic regression model,
the results show that the method of CENC can promote the ability of recognizing
essential proteins, by comparing with the existing methods DC, BC, EC, SC, LAC,
NC and the recent method UC.
