English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Essential protein plays a crucial role in the process of cell life. Theidentification of essential proteins can not only promote the development ofdrug target technology, but also contribute to the mechanism of biologicalevolution. There are plenty of scholars who pay attention to discoveringessential proteins according to the topological structure of protein networkand biological information. The accuracy of protein recognition still demandsto be improved. In this paper, we propose a method which integrate theclustering coefficient in protein complexes and topological properties todetermine the essentiality of proteins. First, we give the definition ofIn-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 asexperimental materials. Through some experiments of logistic regression model,the results show that the method of CENC can promote the ability of recognizingessential proteins, by comparing with the existing methods DC, BC, EC, SC, LAC,NC and the recent method UC.

A mixed clustering coefficient centrality for identifying essential proteins