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In this paper, a similarity measure between genes with protein-protein interactions is proposed. On the basis of it, the combined dissimilarity measure is defined. The combined distance measure is introduced into K-means method, which can be considered as an improved K-means method. The improved K-means method and other three clustering methods are evaluated by a real dataset. Performance of these methods is assessed by a prediction accuracy analysis through known gene annotations. Our results show that the improved K-means method outperforms other clustering methods. The performance of the improved K-means method is also tested by varying the tuning parameter of the combined dissimilarity measure. The results show that when the tuning parameter decreases, the performance increases. Finally, a framework of integration of various biological prior knowledge and gene expression data is proposed.