Protein Function Prediction by Clustering of Protein-Protein Interaction Network

The recent advent of high throughput methods has generated large amounts of protein-protein interaction network (PPIN) data. When studying the workings of a biological cell, it is useful to be able to detect known and predict still undiscovered protein complexes within the cell's PPINs. Such predictions may be used as an inexpensive tool to direct biological experiments. Because of its importance in the studies of protein interaction network, there are different models and algorithms in identifying functional modules in PPINs. In this paper, we present two representative methods, focusing on the comparison of their clustering properties in PPIN and their contribution towards function prediction. The work is done with PPIN data from the bakers' yeast (Saccaromyces cerevisiae) and since the network is noisy and still incomplete, we use pre-processing and purifying. As a conclusion new progress and future research directions are discussed.