Discovering protein complexes from protein-protein interaction data by dense subgraph

Abstract

High-throughput techniques, such as the yeast-two-hybrid system, produce mass protein-protein interaction data. The new technique makes it possible to predict protein complexes by computation. A novel method, named DSDA, has been put forward to predict protein complexes via dense subgraph because the proteins among a protein complex have a much tighter relation among them than with others. This method chooses a node with its neighbors to form the initial subgraph, and chooses a node which has the tightest relation with the subgraph according to greedy strategy, then the chosen node is added into the initial subgraph until the subgraph density is below the threshold value. The obtained subgraph is then removed from the network and the process continues until no subgraph can be detected. Compared with other algorithms, DSDA can predict not only non-overlap protein complexes but also overlap protein complexes. The experiment results show that DSDA predict as many protein complexes as possible. And in Y78K network the accuracy of DSDA is as twice times as that of RNSC and MCL.