Linked Life Data

15961461

Source:http://linkedlifedata.com/resource/pubmed/id/15961461

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:dateCreated
2005-6-17
pubmed:abstractText
MOTIVATION: Microarray designs containing millions to hundreds of millions of probes that tile entire genomes are currently being released. Within the next 2 months, our group will release a microarray data set containing over 12,000,000 microarray measurements taken from 37 mouse tissues. A problem that will become increasingly significant in the upcoming era of genome-wide exon-tiling microarray experiments is the removal of cross-hybridization noise. We present a probabilistic generative model for cross-hybridization in microarray data and a corresponding variational learning method for cross-hybridization compensation, GenXHC, that reduces cross-hybridization noise by taking into account multiple sources for each mRNA expression level measurement, as well as prior knowledge of hybridization similarities between the nucleotide sequences of microarray probes and their target cDNAs. RESULTS: The algorithm is applied to a subset of an exon-resolution genome-wide Agilent microarray data set for chromosome 16 of Mus musculus and is found to produce statistically significant reductions in cross-hybridization noise. The denoised data is found to produce enrichment in multiple gene ontology-biological process (GO-BP) functional groups. The algorithm is found to outperform robust multi-array analysis, another method for cross-hybridization compensation.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
Jun
pubmed:issn
1367-4803
pubmed:author
pubmed:issnType
Print
pubmed:volume
21 Suppl 1
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
i222-31
pubmed:dateRevised
2006-11-15
pubmed:meshHeading
pubmed:year
2005
pubmed:articleTitle
GenXHC: a probabilistic generative model for cross-hybridization compensation in high-density genome-wide microarray data.
pubmed:affiliation
Probabilistic and Statistical Inference Group, Department of Electrical and Computer Engineering, University of Toronto Toronto, ON, Canada M5S 3G4. jim@psi.toronto.edu
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't