@article{EnhancingBindingSitePrediction,
  abstract = "A problem faced by many algorithms for finding transcription factor (TF) binding sites is the high number of false positive hits that result with the increased sensitivity of their prediction. A main contributing factor to this is the short and degenerate nature of these sites which results in a low signal-to-noise ratio. In order to counter this problem, one needs to look beyond the assumption that individual bases of a TF binding site act independently from each other when binding to a transcription factor. In this paper, we present a new method based on templates, designed to exploit the discriminatory features, nucleotide polymorphism, and structural homology present in TF binding sites for distinguishing them from nonbinding sites.",
  author = "S. Gunewardena, P. Jeavons, Z. Zhang",
  doi = "10.1089/cmb.2006.13.929",
  journal = "Journal of Computational Biology",
  number = "4",
  pages = "929-945",
  title = "Enhancing the prediction of transcription factor binding sites by incorporating structural properties and nucleotide covariations",
  url = "http://www.liebertonline.com/doi/abs/10.1089/cmb.2006.13.929",
  volume = "13",
  year = "2006",
}