@Article{biobayesnet2007,
  author =	 {Nikolajewa, Swetlana and Pudimat, Rainer and Hiller, 
                  Michael and Platzer, Matthias and Backofen, Rolf},
  title =	 {{BioBayesNet}: a web server for feature extraction and 
                  {Bayesian} network modeling of biological sequence data},
  journal =	 NAR,
  year =	 2007,
  volume =	 35,
  number =	 {Web Server issue},
  pages =	 {W688-93},
  user =	 {backofen},
  pmid =	 17537825,
  doi = 	 {10.1093/nar/gkm292},
  abstract =	 {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/.}
}