FAC-PIN: Fast agglomerative clustering method for functional modules and protein complex identification in PINs

Abstract

Proteins are known to interact with each other to perform 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 from 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 biologically. Also, a major limitation of agglomerative techniques is that their computational efficiency do not scale well to large protein interaction networks (PINs). In this paper, we propose a new agglomerative algorithm, FAC-PIN, based on a local premetric of relative vertex-to-vertex clustering value and which addresses the above two issues. Our proposed FAC-PIN method has been applied to PIN of baker's yeast for validating functional modules and protein complexes. 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 with HC-PIN and CNM algorithms. Our algorithm outperformed above 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.