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J Comput Biol. 2015 Jan;22(1):25-36. doi: 10.1089/cmb.2014.0175.

Probabilistic generation of random networks taking into account information on motifs occurrence.

Author information

1
1 Université de Technologie de Compiègne and Institut National de l'Environnement Industriel et des Risques, France .

Abstract

Because of the huge number of graphs possible even with a small number of nodes, inference on network structure is known to be a challenging problem. Generating large random directed graphs with prescribed probabilities of occurrences of some meaningful patterns (motifs) is also difficult. We show how to generate such random graphs according to a formal probabilistic representation, using fast Markov chain Monte Carlo methods to sample them. As an illustration, we generate realistic graphs with several hundred nodes mimicking a gene transcription interaction network in Escherichia coli.

KEYWORDS:

biological network; graphical model; network motif; prior information

PMID:
25493547
PMCID:
PMC4283061
DOI:
10.1089/cmb.2014.0175
[Indexed for MEDLINE]
Free PMC Article

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