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Pluripotent stem cells in mice
Institute for Biostatistics and Informatics in Medicine and Aging Research, University of Rostock, Germany.
Institute for Computer Science, University of Onsnabrueck, Germany.
Institute for Biostatistics and Informatics in Medicine and Aging Research, University of Rostock, Germany; Department of Intelligent Science, University of Ljubljana,Slovenia.
Leibniz Institute for Farm Animal Biology, Dummerstorf, Germany.ORCID iD: 0000-0002-7173-5579
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2012 (English)In: Quality of Life through Quality of Information / [ed] J. Mandas et al, Amsterdam, Netherlands: IOS Press, 2012, Vol. 180, 1159-61 p.Conference paper, (Refereed)
Abstract [en]

Pluripotent stem cells are able to self-renew and to differentiate into all adult cell types. Many studies report data describing these cells and characterize them in molecular terms. Gene expression data of pluripotent and non-pluripotent cells from mouse were assembled. Machine learning was applied to classify samples into pluripotent and non-pluripotent cells. To identify minimal sets of best biomarkers, three methods were used: information gain, random forests, and genetic algorithm.

Place, publisher, year, edition, pages
Amsterdam, Netherlands: IOS Press, 2012. Vol. 180, 1159-61 p.
Series
Studies in Health Technology and Informatics, ISSN 0926-9630 ; 180
Keyword [en]
Bioinformatics, machine learning, pluripotency
National Category
Bioinformatics and Systems Biology
Identifiers
URN: urn:nbn:se:oru:diva-40615DOI: 10.3233/978-1-61499-101-4-1159ISI: 000335219500235PubMedID: 22874386Scopus ID: 2-s2.0-84872529254ISBN: 978-1-61499-101-4 (electronic)OAI: oai:DiVA.org:oru-40615DiVA: diva2:777928
Conference
24th Medical Informatics in Europe Conference, MIE 2012, Pisa, Italy, 26-29 August 2012.
Available from: 2015-01-09 Created: 2015-01-09 Last updated: 2017-03-06Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
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More styles
Language
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Output format
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