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Pluripotent stem cells in mice
Institute for Biostatistics and Informatics in Medicine and Aging Research, University of Rostock, Rostock, Germany.
Institute for Computer Science, University of Onsnabrueck, Onsnabrueck, Germany.
Institute for Biostatistics and Informatics in Medicine and Aging Research, University of Rostock, Rostock, Germany; Department of Intelligent Science, University of Ljubljana, Ljubljana, Slovenia.
Leibniz Institute for Farm Animal Biology, Dummerstorf, Germany.ORCID-id: 0000-0002-7173-5579
Vise andre og tillknytning
2012 (engelsk)Inngår i: Quality of Life through Quality of Information / [ed] J. Mandas et al, Amsterdam, Netherlands: IOS Press, 2012, Vol. 180, s. 1159-61Konferansepaper, Publicerat paper (Fagfellevurdert)
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.

sted, utgiver, år, opplag, sider
Amsterdam, Netherlands: IOS Press, 2012. Vol. 180, s. 1159-61
Serie
Studies in Health Technology and Informatics, ISSN 0926-9630 ; 180
Emneord [en]
Bioinformatics, machine learning, pluripotency
HSV kategori
Identifikatorer
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 (digital)OAI: oai:DiVA.org:oru-40615DiVA, id: diva2:777928
Konferanse
24th Medical Informatics in Europe Conference, MIE 2012, Pisa, Italy, 26-29 August 2012.
Tilgjengelig fra: 2015-01-09 Laget: 2015-01-09 Sist oppdatert: 2018-09-12bibliografisk kontrollert

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