Prediction of protein complexes based on weighted network
从加权网络中预测蛋白质复合物

The computational methods predicting protein complexes in the protein-protein interaction network have a great error because of the high false positive rate and false negative rate of protein interaction data. To compensate for this, this paper constructed a new weighted protein interaction network via the integration of the protein-protein interaction and gene expression data. To evaluate the biological significance of the network, it used MCL algorithm to detect protein complexes in the weighted network and unweighted one. Matching analysis was performed between the derived complexes and benchmark complexes. The results show that the weighted network outperforms the unweighted network on biology.