Linked Life Data

10902194

Source:http://linkedlifedata.com/resource/pubmed/id/10902194

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:dateCreated
2000-8-29
pubmed:abstractText
This work describes ANN-Spec, a machine learning algorithm and its application to discovering un-gapped patterns in DNA sequence. The approach makes use of an Artificial Neural Network and a Gibbs sampling method to define the Specificity of a DNA-binding protein. ANN-Spec searches for the parameters of a simple network (or weight matrix) that will maximize the specificity for binding sequences of a positive set compared to a background sequence set. Binding sites in the positive data set are found with the resulting weight matrix and these sites are then used to define a local multiple sequence alignment. Training complexity is O(lN) where l is the width of the pattern and N is the size of the positive training data. A quantitative comparison of ANN-Spec and a few related programs is presented. The comparison shows that ANN-Spec finds patterns of higher specificity when training with a background data set. The program and documentation are available from the authors for UNIX systems.
pubmed:grant
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:issn
1793-5091
pubmed:author
pubmed:issnType
Print
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
467-78
pubmed:dateRevised
2007-11-14
pubmed:meshHeading
pubmed:year
2000
pubmed:articleTitle
ANN-Spec: a method for discovering transcription factor binding sites with improved specificity.
pubmed:affiliation
Center for Biological Sequence Analysis, Technical University of Denmark, Lyngby, Denmark. workman@cbs.dtu.dk
pubmed:publicationType
Journal Article, Research Support, U.S. Gov't, P.H.S.