Predicting Transcription Factor Binding Sites Using a Bayesian Allocation Method
نویسندگان
چکیده
منابع مشابه
Motif discovery programs
BayesMD [1] is a probabilistic, Bayesian model for predicting novel transcription factor binding sites. Biological information about binding sites properties, background sequence models, occurrence and positional preferences are built into the model in modular fashion. Mixture prior parameters for the motif and background are trained using information on TFBSs and organismspecific promoter sequ...
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A Bayesian method for sampling from the distribution of matches to a precompiled transcription factor binding site (TFBS) sequence pattern (conditioned on an observed nucleotide sequence and the sequence pattern) is described. The method takes a position frequency matrix as input for a set of representative binding sites for a transcription factor and two sets of noncoding, 5' regulatory sequen...
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