title: DIA-MCIS: an importance sampling network randomizer for network motif discovery and other topological observables in transcription networks.
abstract: BACKGROUND
  Transcription networks, and other directed networks can be characterized by some topological observables (e.g. network motifs), that require a suitable randomized network ensemble, typically with the same degree sequences of the original ones. The commonly used algorithms sometimes have long convergence times, and sampling problems. We present here an alternative, based on a variant of the importance sampling Monte Carlo developed by (Chen et al.).
  
  
  BACKGROUND
  The algorithm is available at http://wwwteor.mi.infn.it/bassetti/downloads.html
authors: Università degli Studi di Milano, Dip. Fisica, Via Celoria 16, 20133 Milano, Italy. diana.fusco@studenti.unimi.it
authors: D
authors: D
authors: Fusco
authors: B
authors: B
authors: Bassetti
authors: P
authors: P
authors: Jona
authors: M Cosentino
authors: MC
authors: Lagomarsino
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D008954:Models, Biological; D015233:Models, Statistical; D015398:Signal Transduction / Q000502:physiology*; D012984:Software; D014157:Transcription Factors / Q000378:metabolism*
chemical_list: D014157:Transcription Factors