title: Comparison of module detection algorithms in protein networks and investigation of the biological meaning of predicted modules.
abstract: BACKGROUND
  It is generally acknowledged that a functional understanding of a biological system can only be obtained by an understanding of the collective of molecular interactions in form of biological networks. Protein networks are one particular network type of special importance, because proteins form the functional base units of every biological cell. On a mesoscopic level of protein networks, modules are of significant importance because these building blocks may be the next elementary functional level above individual proteins allowing to gain insight into fundamental organizational principles of biological cells.
  
  
  RESULTS
  In this paper, we provide a comparative analysis of five popular and four novel module detection algorithms. We study these module prediction methods for simulated benchmark networks as well as 10 biological protein interaction networks (PINs). A particular focus of our analysis is placed on the biological meaning of the predicted modules by utilizing the Gene Ontology (GO) database as gold standard for the definition of biological processes. Furthermore, we investigate the robustness of the results by perturbing the PINs simulating in this way our incomplete knowledge of protein networks.
  
  
  CONCLUSIONS
  Overall, our study reveals that there is a large heterogeneity among the different module prediction algorithms if one zooms-in the biological level of biological processes in the form of GO terms and all methods are severely affected by a slight perturbation of the networks. However, we also find pathways that are enriched in multiple modules, which could provide important information about the hierarchical organization of the system.
authors.affiliation: Predictive Medicine and Analytics Lab, Department of Signal Processing, Tampere University of Technology, Tampere, Finland.
authors.forename: Shailesh
authors.initials: S
authors.lastname: Tripathi
authors.affiliation: Centre for Statistical Science and Operational Research, School of Mathematics and Physics, Queen's University Belfast, Belfast, UK.
authors.forename: Salissou
authors.initials: S
authors.lastname: Moutari
authors.affiliation: Institute for Theoretical Informatics, Mathematics and Operations Research, Department of Computer Science, Universität der Bundeswehr München, Munich, Germany.
authors.forename: Matthias
authors.initials: M
authors.lastname: Dehmer
authors.affiliation: Predictive Medicine and Analytics Lab, Department of Signal Processing, Tampere University of Technology, Tampere, Finland. v@bio-complexity.com.|Institute of Biosciences and Medical Technology, Tampere, Finland. v@bio-complexity.com.
authors.forename: Frank
authors.initials: F
authors.lastname: Emmert-Streib
mesh_terms: D000465:Algorithms; D000818:Animals; D016000:Cluster Analysis; D056890:Eukaryota / Q000378:metabolism; D063990:Gene Ontology; D006801:Humans; D025941:Protein Interaction Mapping
keywords: Biological processes; Module detection algorithms; Protein networks