PeC Centrality


PeC, is proposed based on the integration of protein-protein interaction data and gene expression data. The basic ideas behind PeC are as follows:
  1. A highly connected protein is more likely to be essential than a low connected one;
  2. Essential proteins tend to form densely connected clusters;
  3. Essential proteins in the same cluster have a more chance to be co-expressed.
In PeC, a protein’s essentiality is determined by the number of the protein’s neighbors and the probability that the protein is co-clustered and co-expressed with its neighbors. To describe PeC simply and clearly, we provide the following definitions and descriptions. The protein-protein interaction network is represented by an undirected graph G(V, E), where a node v ϵ V represents a protein and an edge e(u, v) ϵ E denotes an interaction between two proteins u and v. Gene expression is the process by which information from a gene is used in the synthesis of a functional gene product. These gene products are often proteins. Of course, there may exist some functional RNAs from non-protein coding genes. Here, we only consider the gene expressions for proteins. For a protein v, its gene expressions with s different times are denoted as Ge(v) = {g(v, 1), g(v, 2), ..., g(v, s)}.
The probability that two proteins are co-clustered and co-expressed is evaluated based on the edge clustering coefficient (ECC) and pearson correlation coefficient (PCC).
New centrality measure PeC by integration of PCC and ECC. It has been proved that there exist a number of protein complexes which play a key role in carrying out biological functionality and the essentiality tends to be a product of a protein complex rather than an individual protein.
Based on the definitions of edge clustering coefficient (ECC) and pearson’s correlation coefficient (PCC), they propose a new centrality measure which is named as PeC. The probability that two proteins are coclustered is described from a topological view and the probability that two proteins are co-clustered is characterized from a biological view. Thus, we defined the probability of paired proteins u and v to be in the same cluster as following:

pc(u, v)= ECC(u, v) × PCC(u, v)

For a protein v, its PeC(v) is defined as the sum of the probabilities that the protein and its neighbors belong to a same cluster:
PeC Centrality
Where Nv denotes the set of all neighbors of node v. The value of PeC(v) is determined by not only how many neighbors the protein has but also how likely it is co-clustered with its neighbors.



  • LI, M., ZHANG, H., WANG, J.-X. & PAN, Y. 2012. A new essential protein discovery method based on the integration of protein-protein interaction and gene expression data. BMC systems biology, 6, 15.


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