A Bayesian Network View on Nested Effects Models. (12th November 2008)

• Record Type: Journal Article
• Authors: Zeller, Cordula; Fröhlich, Holger; Tresch, Achim
• Other Names: Repsilber Dirk Academic Editor.
• Is Part Of: EURASIP journal on bioinformatics and systems biology. Volume 2009(2009)
• Publication Date: 2008-11-12
• Subjects: Bioinformatics -- Periodicals; Systems biology -- Periodicals; Systems Biology; Signal Processing, Computer-Assisted; Bio-informatique; Biologie systémique; Bioinformatics; Systems biology; Systems Biology; Bioinformatics; Electronic journals; Periodical; Fulltext; Internet Resources; Periodicals; Periodicals; 570.285
• DOI: 10.1155/2009/195272
• Languages: English
• ISSNs: 1687-4145

Abstract

Abstract : Nested effects models (NEMs) are a class of probabilistic models that were designed to reconstruct a hidden signalling structure from a large set of observable effects caused by active interventions into the signalling pathway. We give a more flexible formulation of NEMs in the language of Bayesian networks. Our framework constitutes a natural generalization of the original NEM model, since it explicitly states the assumptions that are tacitly underlying the original version. Our approach gives rise to new learning methods for NEMs, which have been implemented in theR / Bioconductor package nem. We validate these methods in a simulation study and apply them to a synthetic lethality dataset in yeast.