Structure learning for Bayesian networks as models of biological networks

Reference:

A. Larjo, I. Shmulevich, and Harri Lähdesmäki. Structure learning for bayesian networks as models of biological networks. In Data Mining for Systems Biology, Methods in Molecular Biology, 2012.

Abstract:

Bayesian networks are probabilistic graphical models suitable for model- ing 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, e.g., 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.

Suggested BibTeX entry:

@inproceedings{Larjo2012:BNreview,
    author = {A. Larjo and I. Shmulevich and Harri L{\"a}hdesm{\"a}ki},
    booktitle = {Data Mining for Systems Biology, Methods in Molecular Biology},
    language = {eng},
    title = {Structure learning for Bayesian networks as models of biological networks},
    year = {2012},
}