Identification of functional modules in a PPI network by bounded diameter clustering.
Dense subgraphs of Protein-Protein Interaction (PPI) graphs are assumed to be potential functional modules and play an important role in inferring the functional behavior of proteins. Increasing amount of available PPI data implies a fast, accurate approach of biological complex identification. Therefore, there are different models and algorithms in identifying functional modules. This paper describes a new graph theoretic clustering algorithm that detects densely connected regions in a large PPI graph. The method is based on finding bounded diameter subgraphs around a seed node. The algorithm has the advantage of being very simple and efficient when compared with other graph clustering methods. This algorithm is tested on the yeast PPI graph and the results are compared with MCL, Core-Attachment, and MCODE algorithms.
Department of Clinical Sciences, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd., Dallas, TX 75390-9066, USA. nassim.sohaee@utsouthwestern.edu
Nassim
N
Sohaee
Christian V
CV
Forst
D000465:Algorithms; D016000:Cluster Analysis; D019295:Computational Biology; D025941:Protein Interaction Mapping / Q000706:statistics & numerical data*; D029701:Saccharomyces cerevisiae Proteins / Q000378:metabolism
D029701:Saccharomyces cerevisiae Proteins