Complex detection from PPI data using ensemble method

Many algorithms have been proposed recently to detect protein complexes in protein–protein interaction (PPI) networks. Most proteins form complexes to accomplish biological functions such as transcription of DNA, translation of mRNA and cell growth. Since proteins perform their tasks by interacting with each other, determining these protein–protein interactions is an important task. Traditional clustering approaches for protein complex identification cannot deal with noisy and incomplete PPI data and dependent on information from a single source. Since the noise in the interaction datasets hampers the detection of accurate protein complexes, we propose an ensemble approach for protein complex detection that attempts to combine information from Gene Ontology at the time of complex detection. The PPI data network is taken as input by several baseline complex detection algorithms to generate protein complexes. The protein complexes are then subsequently combined by the proposed ensemble using a consensus building module for the purpose of identifying meaningful complexes. The protein complexes thus predicted by the ensemble are evaluated by comparing them to a set of gold standard protein complexes and their biological relevance established using a co-localization score.