Linked Life Data

18793457

Source:http://linkedlifedata.com/resource/pubmed/id/18793457

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:dateCreated
2008-9-16
pubmed:abstractText
In this paper we present preliminary results stemming from a novel application of Markov Models and Support Vector Machines to splice site classification of Intron-Exon and Exon-Intron (5' and 3') splice sites. We present the use of Markov based statistical methods, in a log likelihood discriminator framework, to create a non-summed, fixed-length, feature vector for SVM-based classification. We also explore the use of Shannon-entropy based analysis for automated identification of minimal-size models (where smaller models have known information loss according to the specified Shannon entropy representation). We evaluate a variety of kernels and kernel parameters in the classification effort. We present results of the algorithms for splice-site datasets consisting of sequences from a variety of species for comparison.
pubmed:commentsCorrections
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:issn
1471-2105
pubmed:author
pubmed:issnType
Electronic
pubmed:volume
9 Suppl 9
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
S12
pubmed:dateRevised
2010-9-21
pubmed:meshHeading
pubmed:year
2008
pubmed:articleTitle
Hybrid MM/SVM structural sensors for stochastic sequential data.
pubmed:affiliation
Department of Computer Science, University of New Orleans, LA 70148, USA. broux@cs.uno.edu
pubmed:publicationType
Journal Article