title: Improving protein complex prediction by reconstructing a high-confidence protein-protein interaction network of Escherichia coli from different physical interaction data sources.
abstract: BACKGROUND
  Although different protein-protein physical interaction (PPI) datasets exist for Escherichia coli, no common methodology exists to integrate these datasets and extract reliable modules reflecting the existing biological process and protein complexes. Naïve Bayesian formula is the highly accepted method to integrate different PPI datasets into a single weighted PPI network, but detecting proper weights in such network is still a major problem.
  
  
  RESULTS
  In this paper, we proposed a new methodology to integrate various physical PPI datasets into a single weighted PPI network in a way that the detected modules in PPI network exhibit the highest similarity to available functional modules. We used the co-expression modules as functional modules, and we shown that direct functional modules detected from Gene Ontology terms could be used as an alternative dataset. After running this integrating methodology over six different physical PPI datasets, orthologous high-confidence interactions from a related organism and two AP-MS PPI datasets gained high weights in the integrated networks, while the weights for one AP-MS PPI dataset and two other datasets derived from public databases have converged to zero. The majority of detected modules shaped around one or few hub protein(s). Still, a large number of highly interacting protein modules were detected which are functionally relevant and are likely to construct protein complexes.
  
  
  CONCLUSIONS
  We provided a new high confidence protein complex prediction method supported by functional studies and literature mining.
authors.affiliation: Department of Computer Science, School of Mathematics, Statistics, and Computer Science, University of Tehran, P.O.Box: 14155-6455, Tehran, Iran.
authors.forename: Shirin
authors.initials: S
authors.lastname: Taghipour
authors.affiliation: Department of Computer Science, School of Mathematics, Statistics, and Computer Science, University of Tehran, P.O.Box: 14155-6455, Tehran, Iran.
authors.forename: Peyman
authors.initials: P
authors.lastname: Zarrineh
authors.affiliation: Department of Computer Science, School of Mathematics, Statistics, and Computer Science, University of Tehran, P.O.Box: 14155-6455, Tehran, Iran. mgtabesh@ut.ac.ir.
authors.forename: Mohammad
authors.initials: M
authors.lastname: Ganjtabesh
authors.affiliation: Department of Computer Science, School of Mathematics, Statistics, and Computer Science, University of Tehran, P.O.Box: 14155-6455, Tehran, Iran.
authors.forename: Abbas
authors.initials: A
authors.lastname: Nowzari-Dalini
mesh_terms: D000465:Algorithms; D001426:Bacterial Proteins / Q000737:chemistry / Q000378:metabolism*; D001499:Bayes Theorem; D002846:Chromatography, Affinity; D004926:Escherichia coli / Q000378:metabolism*; D013058:Mass Spectrometry; D025941:Protein Interaction Mapping / Q000379:methods*; D060066:Protein Interaction Maps
chemical_list: D001426:Bacterial Proteins
keywords: Biological networks; Functional networks; Network clustering; Protein complexes; Protein-protein interaction networks