title: Structure learning for Bayesian networks as models of biological networks.
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.
authors.affiliation: Department of Signal Processing, Tampere University of Technology, Tampere, Finland.
authors.forename: Antti
authors.initials: A
authors.lastname: Larjo
authors.forename: Ilya
authors.initials: I
authors.lastname: Shmulevich
authors.forename: Harri
authors.initials: H
authors.lastname: Lähdesmäki
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001499:Bayes Theorem; D003198:Computer Simulation; D016013:Likelihood Functions; D008954:Models, Biological; D015233:Models, Statistical; D012984:Software; D049490:Systems Biology