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Recognition of transcription factor binding sites with variable order Bayesian networks

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One of the problems in DNA sequence analysis is the recognition of cis-regulatory modules and the reconstruction of their evolution. Many of the existing algorithms, including those for phylogenetic footprinting, use statistical models, such as Markov models and Bayesian networks, for the recognition of transcription factor binding sites. One disadvantage of these models is the exponential growth of the number of model parameters with the context length and the resulting danger of over-fitting. In an attempt to circumvent this problem, we propose variable order Bayesian networks, and we find that they can improve the recognition of binding sites of several transcription factors.

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