Linked Life Data

14534184

Source:http://linkedlifedata.com/resource/pubmed/id/14534184

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:dateCreated
2003-10-9
pubmed:abstractText
Many motif finding algorithms apply local search techniques to a set of seeds. For example, GibbsDNA (Lawrence et al. 1993, Science, 262, 208-214) applies Gibbs sampling to random seeds, and MEME (Bailey and Elkan, 1994, Proceedings of the Second International Conference on Intelligent Systems for Molecular Biology (ISMB-94), 28-36) applies the EM algorithm to selected sample strings, i.e. substrings of the sample. In the case of subtle motifs, recent benchmarking efforts show that both random seeds and selected sample strings may never get close to the globally optimal motif. We propose a new approach which searches motif space by branching from sample strings, and implement this idea in both pattern-based and profile-based settings. Our PatternBranching and ProfileBranching algorithms achieve favorable results relative to other motif finding algorithms. Availability: http://www-cse.ucsd.edu/groups/bioinformatics/software.html
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
Oct
pubmed:issn
1367-4811
pubmed:author
pubmed:issnType
Electronic
pubmed:volume
19 Suppl 2
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
ii149-55
pubmed:dateRevised
2009-11-4
pubmed:meshHeading
pubmed:year
2003
pubmed:articleTitle
Finding subtle motifs by branching from sample strings.
pubmed:affiliation
Department of Computer Science and Engineering, University of California at San Diego, La Jolla, CA 92093-0114, USA. aprice@cs.ucsd.edu
pubmed:publicationType
Journal Article