Document Detail


Structure learning for bayesian networks as models of biological networks.
MedLine Citation:
PMID:  23192539     Owner:  NLM     Status:  In-Data-Review    
Abstract/OtherAbstract:
Bayesian networks are probabilistic graphical models suitable for modeling several kinds of biological systems. In many cases, the structure of a Bayesian network represents causal molecular mechanisms or statistical associations of the underlying system. Bayesian networks have been applied, for example, for inferring the structure of many biological networks from experimental data. We present some recent progress in learning the structure of static and dynamic Bayesian networks from data.
Authors:
Antti Larjo; Ilya Shmulevich; Harri Lähdesmäki
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Publication Detail:
Type:  Journal Article    
Journal Detail:
Title:  Methods in molecular biology (Clifton, N.J.)     Volume:  939     ISSN:  1940-6029     ISO Abbreviation:  Methods Mol. Biol.     Publication Date:  2013  
Date Detail:
Created Date:  2012-11-29     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  9214969     Medline TA:  Methods Mol Biol     Country:  United States    
Other Details:
Languages:  eng     Pagination:  35-45     Citation Subset:  IM    
Affiliation:
Department of Signal Processing, Tampere University of Technology, Tampere, Finland.
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