liu.seSök publikationer i DiVA
Ändra sökning
RefereraExporteraLänk till posten
Permanent länk

Direktlänk
Referera
Referensformat
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Annat format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annat språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf
A bayesian random fragment insertion model for de novo detection of DNA regulatory binding regions
Department of Mathematics, Åbo Akademi University, Åbo, Finland.
Linköpings universitet, Matematiska institutionen, Matematisk statistik. Linköpings universitet, Tekniska högskolan.
Linköpings universitet, Matematiska institutionen, Matematisk statistik. Linköpings universitet, Tekniska högskolan.
2007 (Engelska)Manuskript (preprint) (Övrigt vetenskapligt)
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.

Ort, förlag, år, upplaga, sidor
2007.
Nationell ämneskategori
Matematik
Identifikatorer
URN: urn:nbn:se:liu:diva-13107OAI: oai:DiVA.org:liu-13107DiVA, id: diva2:17845
Tillgänglig från: 2008-03-31 Skapad: 2008-03-31 Senast uppdaterad: 2012-11-21
Ingår i avhandling
1. On approximations and computations in probabilistic classification and in learning of graphical models
Öppna denna publikation i ny flik eller fönster >>On approximations and computations in probabilistic classification and in learning of graphical models
2007 (Engelska)Doktorsavhandling, sammanläggning (Övrigt vetenskapligt)
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.

Ort, förlag, år, upplaga, sidor
Matematiska institutionen, 2007. s. 22
Serie
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 1141
Nyckelord
Mathematical statistics, factorizations, probabilistic classification, nodes, DNA strings
Nationell ämneskategori
Sannolikhetsteori och statistik
Identifikatorer
urn:nbn:se:liu:diva-11429 (URN)978-91-85895-58-8 (ISBN)
Disputation
2007-12-14, Visionen, Hus B, Campus Valla, Linköpings universitet, Linköping, 10:15 (Engelska)
Opponent
Tillgänglig från: 2008-03-31 Skapad: 2008-03-31 Senast uppdaterad: 2012-11-21

Open Access i DiVA

Fulltext saknas i DiVA

Personposter BETA

Ekdahl, MagnusKoski, Timo

Sök vidare i DiVA

Av författaren/redaktören
Ekdahl, MagnusKoski, Timo
Av organisationen
Matematisk statistikTekniska högskolan
Matematik

Sök vidare utanför DiVA

GoogleGoogle Scholar

urn-nbn

Altmetricpoäng

urn-nbn
Totalt: 451 träffar
RefereraExporteraLänk till posten
Permanent länk

Direktlänk
Referera
Referensformat
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Annat format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annat språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf