Detecting protein complexes from protein-protein interaction (PPI) network is 
becoming a difficult challenge in computational biology. There is ample evidence 
that many disease mechanisms involve protein complexes, and being able to 
predict these complexes is important to the characterization of the relevant 
disease for diagnostic and treatment purposes. This article introduces a novel 
method for detecting protein complexes from PPI by using a protein ranking 
algorithm (ProRank). ProRank quantifies the importance of each protein based on 
the interaction structure and the evolutionarily relationships between proteins 
in the network. A novel way of identifying essential proteins which are known 
for their critical role in mediating cellular processes and constructing protein 
complexes is proposed and analyzed. We evaluate the performance of ProRank using 
two PPI networks on two reference sets of protein complexes created from Munich 
Information Center for Protein Sequence, containing 81 and 162 known complexes, 
respectively. We compare the performance of ProRank to some of the well known 
protein complex prediction methods (ClusterONE, CMC, CFinder, MCL, MCode and 
Core) in terms of precision and recall. We show that ProRank predicts more 
complexes correctly at a competitive level of precision and recall. The level of 
the accuracy achieved using ProRank in comparison to other recent methods for 
detecting protein complexes is a strong argument in favor of the proposed 
method.