FAC-PIN: An efficient and fast agglomerative clustering algorithm for protein interaction networks to predict protein complexes and functional modules

Proteins are known to interact with each other to perform specific living organism functions by forming functional modules or protein complexes. Many community detection methods have been devised for the discovery of functional modules or protein complexes in protein interaction networks. One common problem in current agglomerative community detection approaches is that vertices with just one neighbor are often classified as separated clusters, which does not make sense for module or complex identification. In this thesis, we propose a new agglomerative algorithm, FAC-PIN, based on a local premetric of relative vertex-to-vertex clustering value. Our proposed FAC-PIN method is applied to PINs from different species for validating functional modules and protein complexes generated from FAC-PIN with experimentally verified functional modules and complexes respectively. The preliminary computational results show that FAC-PIN can discover functional modules and protein complexes from PINs more accurately. As well as we have also compared the computational times for different species with HC-PIN and CNM algorithms. Our algorithm outperforms two algorithms. Our FAC-PIN algorithm is faster and accurate algorithm which is the current state-of-the-art agglomerative approach to complex prediction and functional module identification.