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Nucleic Acids Research Advance Access originally published online on May 30, 2007
Nucleic Acids Research 2007 35(Web Server issue):W688-W693; doi:10.1093/nar/gkm292
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Nucleic Acids Research, 2007, Vol. 35, No. suppl_2 W688-W693
© 2007 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Articles

BioBayesNet: a web server for feature extraction and Bayesian network modeling of biological sequence data

Swetlana Nikolajewa1, Rainer Pudimat2, Michael Hiller2, Matthias Platzer3 and Rolf Backofen2,*

1Department of Bioinformatics, Friedrich-Schiller-University Jena, Ernst-Abbe-Platz 2, 07743 Jena, Germany 2Institute of Computer Science, Bioinformatics Group, Albert-Ludwigs-University Freiburg, Georges-Koehler-Allee 106, 79110 Freiburg, Germany and 3Genome Analysis, Leibniz Institute for Age Research - Fritz Lipmann Institute, Beutenbergstr. 11, 07745 Jena, Germany

*To whom correspondence should be addressed. Tel: +49 (761) 203-7461; Fax: +49 (761) 203-7462; Email: backofen{at}informatik.uni-freiburg.de

Received January 31, 2007. Revised April 4, 2007. Accepted April 12, 2007.

BioBayesNet is a new web application that allows the easy modeling and classification of biological data using Bayesian networks. To learn Bayesian networks the user can either upload a set of annotated FASTA sequences or a set of pre-computed feature vectors. In case of FASTA sequences, the server is able to generate a wide range of sequence and structural features from the sequences. These features are used to learn Bayesian networks. An automatic feature selection procedure assists in selecting discriminative features, providing an (locally) optimal set of features. The output includes several quality measures of the overall network and individual features as well as a graphical representation of the network structure, which allows to explore dependencies between features. Finally, the learned Bayesian network or another uploaded network can be used to classify new data. BioBayesNet facilitates the use of Bayesian networks in biological sequences analysis and is flexible to support modeling and classification applications in various scientific fields. The BioBayesNet server is available at http://biwww3.informatik.uni-freiburg.de:8080/BioBayesNet/.

The authors wish it to be known that, in their opinion, the first two authors should be regarded as joint First Authors


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