Unsupervised methods for finding protein complexes from PPI networks
Complex biological systems are often represented as networks and studied computationally. In proteinprotein interaction networks, interactions give rise to certain compounds known as protein complexes. Identifying functional protein complexes is an emerging field of study in system biology. Several machine learning methods have been proposed so far to detect functionally enriched protein complexes responsible for specific biological functions or diseases. We present an empirical study on the different unsupervised approaches towards the identification of such complexes. We report performance of seven popularly known methods against four benchmark datasets in terms of six evaluation parameters. We also highlight some issues and challenges prevailing in this field of research.
unsupervised protein complex ppi network clustering overlapping modules