Stochastic segment models of eukaryotic promoter regions

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Item Type:	Article
Title:	Stochastic segment models of eukaryotic promoter regions
Creators Name:	Ohler, U., Stemmer, G., Harbeck, S. and Niemann, H.
Abstract:	We present a new statistical approach for eukaryotic polymerase II promoter recognition. We apply stochastic segment models in which each state represents a functional part of the promoter. The segments are trained in an unsupervised way. We compare segment models with three and five states with our previous system which modeled the promoters as a whole, i.e. as a single state. Results on the classification of a representative collection of human and D. melanogaster promoter and non-promoter sequences show great improvements. The practical importance is demonstrated on the mining of large contiguous sequences.
Keywords:	Algorithms, Computer Simulation, DNA Polymerase II, Factual Databases, Genome, Genetic Models, Genetic Promoter Regions, Stochastic Processes
Source:	Pacific Symposium on Biocomputing
Page Range:	380-391
Date:	2000
Official Publication:	https://doi.org/10.1142/9789814447331_0036
PubMed:	View item in PubMed

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