Linked Life Data

17917129

Source:http://linkedlifedata.com/resource/pubmed/id/17917129

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
3
pubmed:dateCreated
2007-10-5
pubmed:abstractText
We describe a method for classifying subjects based on functional magnetic-resonance (fMR) data, using a method combining a Bayesian-network classifier with inverse-tree structure (BNCIT), and ensemble learning. The central challenge is to generate a classifier from a small sample of high-dimensional data. The principal strengths of our method include the nonparametric multivariate Bayesian-network representation, and joint performance of feature selection and classification. Preliminary results indicate that this method can detect regions characterizing group differences, and can, on the basis of activation levels in these regions, accurately classify new subjects.
pubmed:grant
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:issn
1539-2791
pubmed:author
pubmed:issnType
Print
pubmed:volume
5
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
178-88
pubmed:meshHeading
pubmed:year
2007
pubmed:articleTitle
Clinical diagnosis based on bayesian classification of functional magnetic-resonance data.
pubmed:affiliation
Department of Radiology, University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA, USA. rong.chen@uphs.upenn.edu
pubmed:publicationType
Journal Article, Research Support, N.I.H., Extramural