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DNA segmentation as a model selection process
Author(s) -
Wentian Li
Publication year - 2001
Language(s) - English
Resource type - Conference proceedings
ISBN - 1-58113-353-7
DOI - 10.1145/369133.369202
Subject(s) - bayesian information criterion , recursion (computer science) , segmentation , selection (genetic algorithm) , computer science , model selection , limit (mathematics) , process (computing) , bayesian probability , minimum description length , information criteria , domain (mathematical analysis) , algorithm , artificial intelligence , pattern recognition (psychology) , mathematics , mathematical analysis , operating system
Previous divide-and-conquer segmentation analyses of DNA sequences do not provide a satisfactory stopping criterion for the recursion. This paper proposes that segmentation be considered as a model selection process. Using the tools in model selection, a limit for the stopping criterion on the relaxed end can be determined. The Bayesian information criterion, in particular, provides a much more stringent stopping criterion than what is currently used. Such a stringent criterion can be used to delineate larger DNA domains. A relationship between the stopping criterion and the average domain size is empirically determined, which may aid in the determination of isochore borders.

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