title: Identifying functional modules in interaction networks through overlapping Markov clustering.
abstract: BACKGROUND
  In recent years, Markov clustering (MCL) has emerged as an effective algorithm for clustering biological networks-for instance clustering protein-protein interaction (PPI) networks to identify functional modules. However, a limitation of MCL and its variants (e.g. regularized MCL) is that it only supports hard clustering often leading to an impedance mismatch given that there is often a significant overlap of proteins across functional modules.
  
  
  RESULTS
  In this article, we seek to redress this limitation. We propose a soft variation of Regularized MCL (R-MCL) based on the idea of iteratively (re-)executing R-MCL while ensuring that multiple executions do not always converge to the same clustering result thus allowing for highly overlapped clusters. The resulting algorithm, denoted soft regularized Markov clustering, is shown to outperform a range of extant state-of-the-art approaches in terms of accuracy of identifying functional modules on three real PPI networks.
  
  
  BACKGROUND
  All data and codes are freely available upon request.
  
  
  BACKGROUND
  srini@cse.ohio-state.edu
  
  
  BACKGROUND
  Supplementary data are available at Bioinformatics online.
authors.affiliation: Department of Computer Science and Engineering, the Ohio State University, Columbus, OH 43210-1277, USA.
authors.forename: Yu-Keng
authors.initials: YK
authors.lastname: Shih
authors.forename: Srinivasan
authors.initials: S
authors.lastname: Parthasarathy
mesh_terms: D000465:Algorithms; D016000:Cluster Analysis; D008390:Markov Chains; D025941:Protein Interaction Mapping / Q000379:methods*; D029701:Saccharomyces cerevisiae Proteins / Q000378:metabolism
chemical_list: D029701:Saccharomyces cerevisiae Proteins