A new algorithm for essential proteins identification based on the integration of protein complex co-expression information and edge clustering coefficient.
Essential proteins provide valuable information for the development of biology and medical research from the system level. The accuracy of topological centrality only based methods is deeply affected by noise in the network. Therefore, exploring efficient methods for identifying essential proteins would be of great value. Using biological features to identify essential proteins is efficient in reducing the noise in PPI network. In this paper, based on the consideration that essential proteins evolve slowly and play a central role within a network, a new algorithm, named CED, is proposed. CED mainly employs gene expression level, protein complex information and edge clustering coefficient to predict essential proteins. The performance of CED is validated based on the yeast Protein-Protein Interaction (PPI) network obtained from DIP database and BioGRID database. The prediction accuracy of CED outperforms other seven algorithms when applied to the two databases.
Jiawei
J
Luo
Juan
J
Wu
D000465:Algorithms; D030562:Databases, Protein; D005786:Gene Expression Regulation / Q000502:physiology*; D046912:Multiprotein Complexes / Q000096:biosynthesis / Q000235:genetics; D012441:Saccharomyces cerevisiae / Q000235:genetics / Q000378:metabolism; D029701:Saccharomyces cerevisiae Proteins / Q000096:biosynthesis / Q000235:genetics
D046912:Multiprotein Complexes; D029701:Saccharomyces cerevisiae Proteins