title: Identification of Essential Proteins Based on a New Combination of Local Interaction Density and Protein Complexes.
abstract: BACKGROUND
  Computational approaches aided by computer science have been used to predict essential proteins and are faster than expensive, time-consuming, laborious experimental approaches. However, the performance of such approaches is still poor, making practical applications of computational approaches difficult in some fields. Hence, the development of more suitable and efficient computing methods is necessary for identification of essential proteins.
  
  
  METHODS
  In this paper, we propose a new method for predicting essential proteins in a protein interaction network, local interaction density combined with protein complexes (LIDC), based on statistical analyses of essential proteins and protein complexes. First, we introduce a new local topological centrality, local interaction density (LID), of the yeast PPI network; second, we discuss a new integration strategy for multiple bioinformatics. The LIDC method was then developed through a combination of LID and protein complex information based on our new integration strategy. The purpose of LIDC is discovery of important features of essential proteins with their neighbors in real protein complexes, thereby improving the efficiency of identification.
  
  
  RESULTS
  Experimental results based on three different PPI(protein-protein interaction) networks of Saccharomyces cerevisiae and Escherichia coli showed that LIDC outperformed classical topological centrality measures and some recent combinational methods. Moreover, when predicting MIPS datasets, the better improvement of performance obtained by LIDC is over all nine reference methods (i.e., DC, BC, NC, LID, PeC, CoEWC, WDC, ION, and UC).
  
  
  CONCLUSIONS
  LIDC is more effective for the prediction of essential proteins than other recently developed methods.
authors.affiliation: College of Computer Science and Electronic Engineering, Hunan University, Changsha, China.
authors.forename: Jiawei
authors.initials: J
authors.lastname: Luo
authors.affiliation: College of Computer Science and Electronic Engineering, Hunan University, Changsha, China.
authors.forename: Yi
authors.initials: Y
authors.lastname: Qi
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D004926:Escherichia coli / Q000235:genetics*; D029968:Escherichia coli Proteins / Q000235:genetics; D020043:Genes, Essential / Q000235:genetics*; D046912:Multiprotein Complexes / Q000235:genetics; D025941:Protein Interaction Mapping / Q000379:methods; D060066:Protein Interaction Maps / Q000235:genetics*; D012441:Saccharomyces cerevisiae / Q000235:genetics*; D029701:Saccharomyces cerevisiae Proteins / Q000235:genetics
chemical_list: D029968:Escherichia coli Proteins; D046912:Multiprotein Complexes; D029701:Saccharomyces cerevisiae Proteins