Journal Article

BNFinder: exact and efficient method for learning Bayesian networks

Bartek Wilczyński and Norbert Dojer

in Bioinformatics

Volume 25, issue 2, pages 286-287
Published in print January 2009 | ISSN: 1367-4803
Published online September 2008 | e-ISSN: 1460-2059 | DOI:

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Motivation: Bayesian methods are widely used in many different areas of research. Recently, it has become a very popular tool for biological network reconstruction, due to its ability to handle noisy data. Even though there are many software packages allowing for Bayesian network reconstruction, only few of them are freely available to researchers. Moreover, they usually require at least basic programming abilities, which restricts their potential user base. Our goal was to provide software which would be freely available, efficient and usable to non-programmers.

Results: We present a BNFinder software, which allows for Bayesian network reconstruction from experimental data. It supports dynamic Bayesian networks and, if the variables are partially ordered, also static Bayesian networks. The main advantage of BNFinder is the use exact algorithm, which is at the same time very efficient (polynomial with respect to the number of observations).

Availability: The software, supplementary information and manual is available at Besides the availability of the standalone application and the source code, we have developed a web interface to BNFinder application running on our servers. A web tutorial on different options of BNFinder is also available.


Journal Article.  1471 words. 

Subjects: Bioinformatics and Computational Biology

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