Structure Learning for Bayesian Networks as Models of Biological Networks
Antti Larjo, Ilya Shmulevich, Harri J Lähdesmäki · Methods in molecular biology · 2012
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.