Identifying protein complexes from a PPI network is crucial to understand principles of cellular organization and functional mechanisms. However, it is still a difficult task because protein complexes have various topologies in PPI networks. In the paper, a novel protein complex identifying method, named LF-PIN, is proposed based on local fitness method. Firstly, LF-PIN calculates each PPI's weight based on its clustering value in the PPI network and selects seed edges by the edge weight. Then, protein complexes are extended from seed edges based on the evaluation of their neighbors' fitness values until their fitness reach the local maximum value. We apply the proposed algorithm LF-PIN and other nine previous algorithms, including HC-PIN, NFC, MCODE, DPClus, IPCA, CPM, MCL, CMC and Core-Attachment, to the PPI network of S.cerevisiae and compare their performances. Experimental results show that LF-PIN outperforms other competing algorithms in terms of matching with known complexes and functional enrichment.
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