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A bayesian random fragment insertion model for de novo detection of DNA regulatory binding regions
Department of Mathematics, Åbo Akademi University, Åbo, Finland.
Linköping University, Department of Mathematics, Mathematical Statistics . Linköping University, The Institute of Technology.
Linköping University, Department of Mathematics, Mathematical Statistics . Linköping University, The Institute of Technology.
2007 (English)Manuscript (preprint) (Other academic)
Abstract [en]

Identification of regulatory binding motifs within DNA sequences is a commonly occurring problem in computationnl bioinformatics. A wide variety of statistical approaches have been proposed in the literature to either scan for previously known motif types or to attempt de novo identification of a fixed number (typically one) of putative motifs. Most approaches assume the existence of reliable biodatabasc information to build probabilistic a priori description of the motif classes. No method has been previously proposed for finding the number of putative de novo motif types and their positions within a set of DNA sequences. As the number of sequenced genomes from a wide variety of organisms is constantly increasing, there is a clear need for such methods. Here we introduce a Bayesian unsupervised approach for this purpose by using recent advances in the theory of predictive classification and Markov chain Monte Carlo computation. Our modelling framework enables formal statistical inference in a large-scale sequence screening and we illustrate it by a set of examples.

Place, publisher, year, edition, pages
2007.
National Category
Mathematics
Identifiers
URN: urn:nbn:se:liu:diva-13107OAI: oai:DiVA.org:liu-13107DiVA: diva2:17845
Available from: 2008-03-31 Created: 2008-03-31 Last updated: 2012-11-21
In thesis
1. On approximations and computations in probabilistic classification and in learning of graphical models
Open this publication in new window or tab >>On approximations and computations in probabilistic classification and in learning of graphical models
2007 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Model based probabilistic classification is heavily used in data mining and machine learning. For computational learning these models may need approximation steps however. One popular approximation in classification is to model the class conditional densities by factorization, which in the independence case is usually called the ’Naïve Bayes’ classifier. In general probabilistic independence cannot model all distributions exactly, and not much has been published on how much a discrete distribution can differ from the independence assumption. In this dissertation the approximation quality of factorizations is analyzed in two articles.

A specific class of factorizations is the factorizations represented by graphical models. Several challenges arise from the use of statistical methods for learning graphical models from data. Examples of problems include the increase in the number of graphical model structures as a function of the number of nodes, and the equivalence of statistical models determined by different graphical models. In one article an algorithm for learning graphical models is presented. In the final article an algorithm for clustering parts of DNA strings is developed, and a graphical representation for the remaining DNA part is learned.

Place, publisher, year, edition, pages
Matematiska institutionen, 2007. 22 p.
Series
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 1141
Keyword
Mathematical statistics, factorizations, probabilistic classification, nodes, DNA strings
National Category
Probability Theory and Statistics
Identifiers
urn:nbn:se:liu:diva-11429 (URN)978-91-85895-58-8 (ISBN)
Public defence
2007-12-14, Visionen, Hus B, Campus Valla, Linköpings universitet, Linköping, 10:15 (English)
Opponent
Available from: 2008-03-31 Created: 2008-03-31 Last updated: 2012-11-21

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Ekdahl, MagnusKoski, Timo

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