Inference algorithms for gene networks: a statistical mechanics analysis

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dc.creatorBraunstein, A.
dc.creatorPagnani, A.
dc.creatorWeigt, M.
dc.creatorZecchina, R.
dc.date2008-12-04
dc.date.accessioned2026-07-07T12:09:24Z
dc.date.available2026-07-07T12:09:24Z
dc.descriptionThe inference of gene regulatory networks from high throughput gene expression data is one of the major challenges in systems biology. This paper aims at analysing and comparing two different algorithmic approaches. The first approach uses pairwise correlations between regulated and regulating genes; the second one uses message-passing techniques for inferring activating and inhibiting regulatory interactions. The performance of these two algorithms can be analysed theoretically on well-defined test sets, using tools from the statistical physics of disordered systems like the replica method. We find that the second algorithm outperforms the first one since it takes into account collective effects of multiple regulators.
dc.identifierhttps://arxiv.org/abs/0812.0940
dc.identifierhttp://arxiv.org/abs/0812.0940
dc.identifierJ. Stat. Mech. (2008) P12001
dc.identifierdoi:10.1088/1742-5468/2008/12/P12001
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/209612
dc.subjectQuantitative Methods
dc.subjectDisordered Systems and Neural Networks
dc.titleInference algorithms for gene networks: a statistical mechanics analysis
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