High-throughput experimental technologies, along with computational predictions, 
have promoted the emergence of large-scale interactome for numerous organisms. 
Identification of protein complexes from these interactome networks is crucial 
to understand principles of cellular organization and predict protein functions. 
Protein complexes are generally considered as dense subgraphs. However, the real 
protein complexes do not always have highly connected topologies. In this paper, 
a novel protein complex identifying method, named EPOF, is proposed, using 
essential proteins and the local metric of vertex fitness. In EPOF, cliques in 
the subnetwork which is consisted by the essential proteins are firstly 
considered as seeds, which are ordered according to their size and the number of 
their neighbors. A protein complex is extended from a seed based on the 
evaluation of its neighbors' fitness value. Then, the similar procedure is 
applied to the cliques identified in the subnetwork which is consisted by the 
proteins which is not clustered in the first step. When EPOF identifies 
complexes by expanding essential protein cliques, the essential proteins have 
higher priority and lower threshold. When it identifies complexes by expanding 
nonessential protein cliques, the nonessential proteins have higher priority and 
lower threshold. Finally, the last step, we output the identified complexes set. 
The proposed algorithm EPOF is applied to the unweighted and weighted 
interaction networks of S. cerevisiae and detects many well known protein 
complexes. We compare the performances of EPOF to other ten previous algorithms, 
including EAGLE, NFC, MCODE, DPClus, IPCA, CPM, MCL, CMC, SPICi, and 
Core-Attachment. Experimental results show that EPOF outperforms other previous 
competing algorithms in terms of matching with known complexes, sensitivity, 
specificity, f-measure, function enrichment and accuracy. The program and 
related files available on https://github.com/gangchen/epof.