# EGC - Edge clustering coefficient and Gene ontology information’s Combination method

#### Definition

Gene ontology (GO) information is adopted as a
measure to evaluate the reliability of the edges in PPI network and a new algorithm
EGC is proposed to identify essential proteins by integrating the topological features
of the PPI network and the information of GO.

The essentiality of each protein u in PPI network is determined by: Here, t is a proportionality parameter which takes value in the range of 0 to 1, N

The GO similarity between protein u and protein v is

The edge clustering coefficient of an edge (u, v), connecting node u and node v, can be defined by the following formula:

The essentiality of each protein u in PPI network is determined by: Here, t is a proportionality parameter which takes value in the range of 0 to 1, N

_{u}is the set which contains all the neighbors of u.**GO similarity**The GO similarity between protein u and protein v is

*GO–similarity(u, v) = GO(u) ∩ GO(v)*

**Edge Clustering Coefficient**The edge clustering coefficient of an edge (u, v), connecting node u and node v, can be defined by the following formula:

_{u}and k_{v}are the degrees of node u and node v, respectively. z_{u,v}^{(3)}means the number of triangles constituted by the edge (u, v) in the network and min(k_{u}− 1, k_{v}− 1) is the maximum possible number of triangles that constituted by the edge (u, v).#### Requirements

Undirected graph G=(V,E), GO database, parameter t and k.

#### Software

#### References

- LUO, J. & ZHANG, N. 2014. Prediction of essential proteins based on edge clustering coefficient and gene ontology information. Journal of Biological Systems, 22, 339-351. DOI: 10.1142/S0218339014500119