An empirical Bayes approach to inferring large-scale gene association networks | Zendy

Juliane Schäfer | Zendy; Korbinian Strimmer | Zendy

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An empirical Bayes approach to inferring large-scale gene association networks

Author(s) -

Juliane Schäfer,

Korbinian Strimmer

Publication year - 2004

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/bti062

Subject(s) - graphical model , bayes' theorem , data mining , inference , computer science , bayesian network , sample size determination , network topology , bayesian probability , machine learning , artificial intelligence , mathematics , statistics , operating system

Genetic networks are often described statistically using graphical models (e.g. Bayesian networks). However, inferring the network structure offers a serious challenge in microarray analysis where the sample size is small compared to the number of considered genes. This renders many standard algorithms for graphical models inapplicable, and inferring genetic networks an 'ill-posed' inverse problem.

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