Studien zur Mustererkennung , Bd. 7
The thesis presents increasingly complex probabilistic models representing the DNA sequence and structure of promoters, and shows how they can be used to identify promoter regions in long DNA seqünces. Among other methods, different types of Markov chain and generalized hidden Markov models are studied, and a Bayesian classification approach is compared to neural networks. The system was successfully applied by the Drosophila Genome Project to the complete genome of the fruit fly, and results are compared with promoter recognition in human sequences.
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