Essential protein discovery method based on integration of PPI and gene expression data

A new method for identifying essential proteins based on the integration of PPI and gene expression data named PeC was proposed.For each edge of the network,its edge clustering coefficient(ECC)and Pearson correlation coefficient(PCC)were calculated.And then,the weight of each edge was computed based on ECC and PCC.Then,a protein's PeC value was defined as the sum of weights of the edges connected to it.The experimental results on the yeast protein interaction network show that PeC is obviously higher than other eight centrality measures(DC,BC,CC,SC,EC, IC,LAC and SoECC)in the prediction accuracy of essential proteins.Especially,for less than the top 10%proteins selected as the candidate essential proteins,the prediction accuracy of PeC has 20%higher than those of SC,CC and EC.