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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\napproaches assume the existence of reliable biodatabase information to build probabilistic a priori description of the motif classes.\nExamples of attempts to do probabilistic unsupervised learning about the number of putative de novo motif types and their\npositions 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.<\/jats:p>","DOI":"10.1155\/2009\/219743","type":"journal-article","created":{"date-parts":[[2009,8,11]],"date-time":"2009-08-11T15:00:00Z","timestamp":1250002800000},"page":"1-11","source":"Crossref","is-referenced-by-count":0,"title":["Bayesian Unsupervised Learning of DNA Regulatory Binding Regions"],"prefix":"10.1155","volume":"2009","author":[{"given":"Jukka","family":"Corander","sequence":"first","affiliation":[{"name":"Department of Mathematics, \u00c5bo Akademi University, 20500 Turku, Finland"}]},{"given":"Magnus","family":"Ekdahl","sequence":"additional","affiliation":[{"name":"Department of Mathematics, University of Link\u00f6ping, 58183 Link\u00f6ping, Sweden"}]},{"given":"Timo","family":"Koski","sequence":"additional","affiliation":[{"name":"Department of Mathematics, The Royal Institute of Technology, 100 44 Stockholm, 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