Linked Life Data

19897095

Source:http://linkedlifedata.com/resource/pubmed/id/19897095

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:dateCreated
2009-11-9
pubmed:abstractText
A wide variety of stem cells has been reported to exist and renew several adult tissues, raising the question of the existence of a stemness signature-that is, a common molecular program of differentiation. To detect such a signature, we applied a data integration algorithm on several DNA microarray datasets generated by the Stem Cell Genome Anatomy Project (SCGAP) Consortium on several mouse and human tissues, to generate a cross-organism compendium that we submitted to a single layer artificial neural network (ANN) trained to attribute differentiation labels-from totipotent stem cells to differentiated ones (five labels in total were used). The inherent architecture of the system allowed studing the biology behind stem cells differentiation stages and the ANN isolated a 63 gene stemness signature. This chapter presents technological details on DNA microarray integration, ANN training through leave-one-out cross-validation, and independent testing on uncharacterized adult tissues by automated detection of differentiation capabilities on human prostate and mouse stomach progenitors. All scripts of the Stem Cell Analysis and characterization by Neural Networks (SCANN) project are available on the SourceForge Web site: http://scann.sourceforge.net.
pubmed:grant
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:issn
1557-7988
pubmed:author
pubmed:issnType
Electronic
pubmed:volume
467
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
229-45
pubmed:meshHeading
pubmed:year
2009
pubmed:articleTitle
Large scale transcriptome data integration across multiple tissues to decipher stem cell signatures.
pubmed:affiliation
Inserm, UMR891, CRCM, Integrative Bioinformatics, Marseille, France.
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't, Research Support, N.I.H., Extramural