title: Comparison of tissue/disease specific integrated networks using directed graphlet signatures.
abstract: BACKGROUND
  Analysis of integrated genome-scale networks is a challenging problem due to heterogeneity of high-throughput data. There are several topological measures, such as graphlet counts, for characterization of biological networks.
  
  
  RESULTS
  In this paper, we present methods for counting small sub-graph patterns in integrated genome-scale networks which are modeled as labeled multidigraphs. We have obtained physical, regulatory, and metabolic interactions between H. sapiens proteins from the Pathway Commons database. The integrated network is filtered for tissue/disease specific proteins by using a large-scale human transcriptional profiling study, resulting in several tissue and disease specific sub-networks. We have applied and extended the idea of graphlet counting in undirected protein-protein interaction (PPI) networks to directed multi-labeled networks and represented each network as a vector of graphlet counts. Graphlet counts are assessed for statistical significance by comparison against a set of randomized networks. We present our results on analysis of differential graphlets between different conditions and on the utility of graphlet count vectors for clustering multiple condition specific networks.
  
  
  CONCLUSIONS
  Our results show that there are numerous statistically significant graphlets in integrated biological networks and the graphlet signature vector can be used as an effective representation of a multi-labeled network for clustering and systems level analysis of tissue/disease specific networks.
authors.affiliation: Department of Medical Informatics, Informatics Institute, Middle East Technical University, Dumlupinar Bulvari No:1, Ankara, 06800, Turkey.
authors.forename: Arzu Burcak
authors.initials: AB
authors.lastname: Sonmez
authors.affiliation: Department of Computer Egineering, Middle East Technical University, Dumlupinar Bulvari No:1, Ankara, 06800, Turkey. tcan@metu.edu.tr.
authors.forename: Tolga
authors.initials: T
authors.lastname: Can
mesh_terms: D003196:Computer Graphics; D003198:Computer Simulation; D004194:Disease; D053263:Gene Regulatory Networks; D006801:Humans; D008954:Models, Biological; D009928:Organ Specificity; D025941:Protein Interaction Mapping / Q000379:methods*; D060066:Protein Interaction Maps; D011506:Proteins / Q000378:metabolism; D020543:Proteome / Q000378:metabolism*; D014584:User-Computer Interface
chemical_list: D011506:Proteins; D020543:Proteome
keywords: Directed graphlets; Integrated networks; Network comparison