Motivation: 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.).

DIA-MCIS: An importance sampling network randomizer for network motif discovery and other topological observables in transcription networks / D. Fusco, B. Bassetti, P. Jona, M. Cosentino lagomarsino. - In: BIOINFORMATICS. - ISSN 1367-4803. - 23:24(2007), pp. 3388-3390. [10.1093/bioinformatics/btm454]

DIA-MCIS: An importance sampling network randomizer for network motif discovery and other topological observables in transcription networks

Bassetti B.;Cosentino lagomarsino M.
2007

Abstract

Motivation: 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.).
Computer Simulation; Data Interpretation, Statistical; Models, Statistical; Signal Transduction; Transcription Factors; Algorithms; Models, Biological; Software
Settore FIS/02 - Fisica Teorica, Modelli e Metodi Matematici
Settore INF/01 - Informatica
Settore BIO/11 - Biologia Molecolare
BIOINFORMATICS
Article (author)
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/2434/802785
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