Gene regulatory network inference with extended scores for Bayesian networks

被引：0

作者：

Vandel, Jimmy ^{[1
]}

Mangin, Brigitte ^{[1
]}

Vignes, Matthieu ^{[1
]}

Leroux, Damien ^{[1
]}

Loudet, Olivier ^{[2
]}

Martin-Magniette, Marie-Laure ^{[3
,4
]}

De Givry, Simon ^{[1
]}

机构：

[1] INRA, UR 875, Unité de Biométrie et Intelligence Artificielle, F-31326, Castanet-Tolosan

[2] INRA, UMR 1318, Institut Jean-Pierre Bourgin, F-78000, Versailles

[3] INRA, UMR 1165, Unité de Recherche en Génomique Végétale, F-91057, Evry

[4] INRA, UMR 518, Mathématiques et Informatique Appliquées, F-75231, Paris

来源：

Revue d'Intelligence Artificielle | 2012年 / 26卷 / 06期

关键词：

Bayesian network; Gene regulation; Genetical genomics; Structure learning;

D O I：

10.3166/RIA.26.679-708

中图分类号：

学科分类号：

摘要：

Inferring gene regulatory networks tends to use several biological information. Here we use data from genetic markers and expression data in the framework of discrete static bayesian networks. We compare several scores and also the impact of a network connectivity a priori. We propose and compare two models with existing approaches of gene regulatory network inference. On simulated data one of our models reached better results in the case of small sample size. We use this model on real data in Arabidopsis thaliana. © 2012 Lavoisier.

引用

页码：679 / 708

页数：29

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