Bayesian Unsupervised Learning of DNA Regulatory Binding Regions
Joint Authors
Ekdahl, Magnus
Corander, Jukka
Koski, Timo
Source
Advances in Artificial Intelligence
Issue
Vol. 2009, Issue 2009 (31 Dec. 2009), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2009-08-11
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Information Technology and Computer Science
Science
Abstract EN
Identification of regulatory binding motifs, that is, short specific words, within DNA sequences is a commonly occurring problem in computational bioinformatics.
A wide variety of probabilistic 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 biodatabase information to build probabilistic a priori description of the motif classes.
Examples of attempts to do probabilistic unsupervised learning about the number of putative de novo motif types and their positions within a set of DNA sequences are very rare in the literature.
Here we show how such a learning problem can be formulated using a Bayesian model that targets to simultaneously maximize the marginal likelihood of sequence data arising under multiple motif types as well as under the background DNA model, which equals a variable length Markov chain.
It is demonstrated how the adopted Bayesian modelling strategy combined with recently introduced nonstandard stochastic computation tools yields a more tractable learning procedure than is possible with the standard Monte Carlo approaches.
Improvements and extensions of the proposed approach are also discussed.
American Psychological Association (APA)
Corander, Jukka& Ekdahl, Magnus& Koski, Timo. 2009. Bayesian Unsupervised Learning of DNA Regulatory Binding Regions. Advances in Artificial Intelligence،Vol. 2009, no. 2009, pp.1-11.
https://search.emarefa.net/detail/BIM-455638
Modern Language Association (MLA)
Corander, Jukka…[et al.]. Bayesian Unsupervised Learning of DNA Regulatory Binding Regions. Advances in Artificial Intelligence No. 2009 (2009), pp.1-11.
https://search.emarefa.net/detail/BIM-455638
American Medical Association (AMA)
Corander, Jukka& Ekdahl, Magnus& Koski, Timo. Bayesian Unsupervised Learning of DNA Regulatory Binding Regions. Advances in Artificial Intelligence. 2009. Vol. 2009, no. 2009, pp.1-11.
https://search.emarefa.net/detail/BIM-455638
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-455638