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Identifying protein complexes from the large-scale protein interaction network is crucial to understand principles of cellular organization and predict protein functions, which is one of the most important issues in the post-genomic era. Generally, the traditional protein complex discovery algorithms are only based on the protein-protein interaction network (PPI network), and are not so accurate. In this paper, a novel algorithm IPCIPG is proposed based on the integration of the PPI network and the gene expression data. Different from other previous methods which use gene expression data to evaluate the reliability of PPIs, IPCIPG integrates the gene expression data into PPI network during the identification of protein complexes. IPCIPG uses the edge clustering coefficient (ECC) and the co-expression correlation between proteins (PCC) to calculate the weight of each node in the PPI network. And then the node with the highest weight is selected as seed, then, a dense sub-graph will be obtained by extending from the seed. The experiment results on the data of Saccharomyces cerevisiae show that IPCIPG can identify the protein complexes with specific biological meaning more effectively, precisely and comprehensively than the other algorithms HUNTER, HC-PIN, CMC, SPICI, MOCDE, and MCL. [ABSTRACT FROM AUTHOR]