@article{pudimat:2005:bioinformatics,
  author =	 {Rainer Pudimat and E.G. Schukat-Talamazzini and Rolf
                  Backofen},
  title =	 {A multiple-feature framework for modelling and
                  predicting transcription factor binding sites},
  journal =	 {Bioinformatics},
  volume =       {21},
  number =       {14},
  pages =        {3082-8},
  publists =	 {All and Rainer Pudimat and Rolf Backofen},
  year =	 {2005},
  abstract =	 {Motivation: The identification of transcription
                  factor binding sites in promoter sequences is an
                  important problem, since it reveals information
                  about the transcriptional regulation of genes. For
                  analysing transcriptional regulation, computational
                  approaches for predicting putative binding sites are
                  applied. Commonly used stochastic models for binding
                  sites are position specific score matrices (PSSM),
                  which show weak predictive power. Results: We have
                  developed a probabilistic modelling approach which
                  allows to consider diverse characteristic binding
                  site properties to obtain more accurate
                  representations of binding sites. These properties
                  are modelled as random variables in Bayesian
                  networks, which are capable to deal with
                  dependencies amongbinding site properties. Cross
                  validation on several data sets shows improvements
                  in the false positive error rate and the
                  significance (p-value) of true binding
                  sites. Availability: A more extensive description of
                  validation results are available at
                  http://www.bio.inf.uni-jena.de/Software/promapper/
                  <p>Contact: backofen@inf.uni-jena.de},
  user =	 {rpudimat}
}