Structure Learning for Bayesian Networks as Models of Biological Networks View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2012-09-08

AUTHORS

Antti Larjo , Ilya Shmulevich , Harri Lähdesmäki

ABSTRACT

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. More... »

PAGES

35-45

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-1-62703-107-3_4

DOI

http://dx.doi.org/10.1007/978-1-62703-107-3_4

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1018381107

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/23192539


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