Protein complexes play an important role in cellular mechanism. Identification 
of protein complexes in protein-protein interaction (PPI) networks is the first 
step in understanding the organization and dynamics of cell function. Several 
high-throughput experimental techniques produce a large amount of protein 
interactions, which can be used to predict protein complexes in a PPI network. 
We have developed an algorithm PROCOMOSS (Protein Complex Detection using 
Multi-objective Evolutionary Approach based on Semantic Similarity) for 
partitioning the whole PPI network into clusters, which serve as predicted 
protein complexes. We consider both graphical properties of a PPI network as 
well as biological properties based on GO semantic similarity measure as 
objective functions. Here three different semantic similarity measures are used 
for grouping functionally similar proteins in the same clusters. We have applied 
the PROCOMOSS algorithm on two different datasets of Saccharomyces cerevisiae to 
find and predict protein complexes. A real-life application of the PROCOMOSS is 
also shown here by applying it in the human PPI network consisting of 
differentially expressed genes affected by gastric cancer. Gene ontology and 
pathway based analyses are also performed to investigate the biological 
importance of the extracted gene modules.