CoEWC - Co-Expression Weighted by Clustering Coefficient


Definition

CoEWC is based on the integration of the topological properties of PPI network and the coexpression of interacting proteins. It determines a protein’s essentiality based on whether it has a high probability to be co-expressed with its neighbors and whether each of its neighbors takes part in densely connected clusters. In CoEWC, a protein’s essentiality is determined by the number of the protein’s neighbors and the probability that the protein is co-expressed with its neighbors as well as its neighbors’ clustering properties.

Co-Expression Weighted by Clustering Coefficient need two preliminary factors namely Pearson correlation coefficient (PCC) and Clustering coefficient (CC) that will be calculated as fallows:



Where is the number of samples of the gene expression data; (or ) is the expression level of gene (or ) in the sample under a specific condition; (or ) represents the mean expression level of gene (or \gamma) and (or ) represents the standard deviation of expression level of gene (or ).



Where is the set of neighbors of protein and denotes the number of immediately connected neighbors of .



Where denotes the set of all immediately connected neighbors of node in PPI network.


Different from SoECC and PeC, which all emphasize co-clustering relationship between a protein and its neighbors, CoEWC pay more attention to the clustering property of the protein’s neighbors rather than the protein itself.


References

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