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WeightNameValue
1000 Titel
  • Learning Biomarkers of Pluripotent Stem Cells in Mouse
1000 Autor/in
  1. Scheubert, Lena |
  2. Schmidt, Rainer |
  3. Repsilber, Dirk |
  4. Lustrek, Mitja |
  5. Fuellen, Georg |
1000 Erscheinungsjahr 2011
1000 LeibnizOpen
1000 Art der Datei
1000 Publikationstyp
  1. Artikel |
1000 Online veröffentlicht
  • 2011-07-26
1000 Erschienen in
1000 Quellenangabe
  • 4(1): 233-251
1000 FRL-Sammlung
1000 Copyrightjahr
  • 2011
1000 Lizenz
1000 Verlagsversion
  • https://doi.org/10.1093/dnares/dsr016 |
  • https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3158465/ |
1000 Ergänzendes Material
  • https://academic.oup.com/dnaresearch/article/18/4/233/501892#supplementary-data |
1000 Publikationsstatus
1000 Begutachtungsstatus
1000 Sprache der Publikation
1000 Abstract/Summary
  • 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. Machine learning yields classifiers that can accurately identify pluripotent stem cells, but there is a lack of studies yielding minimal sets of best biomarkers (genes/features). We assembled gene expression data of pluripotent stem cells and non-pluripotent cells from the mouse. After normalization and filtering, we applied machine learning, classifying samples into pluripotent and non-pluripotent with high cross-validated accuracy. Furthermore, to identify minimal sets of best biomarkers, we used three methods: information gain, random forests and a wrapper of genetic algorithm and support vector machine (GA/SVM). We demonstrate that the GA/SVM biomarkers work best in combination with each other; pathway and enrichment analyses show that they cover the widest variety of processes implicated in pluripotency. The GA/SVM wrapper yields best biomarkers, no matter which classification method is used. The consensus best biomarker based on the three methods is Tet1, implicated in pluripotency just recently. The best biomarker based on the GA/SVM wrapper approach alone is Fam134b, possibly a missing link between pluripotency and some standard surface markers of unknown function processed by the Golgi apparatus.
1000 Sacherschließung
lokal genetic algorithm
lokal support vector machine
lokal pluripotency
lokal machine learning
lokal feature selection
1000 Fachgruppe
  1. Biologie |
1000 Fächerklassifikation (DDC)
1000 Liste der Beteiligten
  1. https://frl.publisso.de/adhoc/creator/U2NoZXViZXJ0LCBMZW5h|https://frl.publisso.de/adhoc/creator/U2NobWlkdCwgUmFpbmVy|https://frl.publisso.de/adhoc/creator/UmVwc2lsYmVyLCBEaXJr|https://frl.publisso.de/adhoc/creator/THVzdHJlaywgTWl0amE=|https://frl.publisso.de/adhoc/creator/RnVlbGxlbiwgR2Vvcmc=
1000 Förderer
  1. German Research Foundation (DFG) |
1000 Fördernummer
  1. SPP 1356; FU583/2-1
1000 Förderprogramm
  1. Pluripotency and Cellular Reprogramming
1000 Dateien
  1. Learning Biomarkers of Pluripotent Stem Cells in Mouse
1000 Förderung
  1. 1000 joinedFunding-child
    1000 Förderer German Research Foundation (DFG) |
    1000 Förderprogramm Pluripotency and Cellular Reprogramming
    1000 Fördernummer SPP 1356; FU583/2-1
1000 Objektart article
1000 Beschrieben durch
1000 @id frl:6410740.rdf
1000 Erstellt am 2018-10-23T13:26:26.078+0200
1000 Erstellt von 122
1000 beschreibt frl:6410740
1000 Bearbeitet von 218
1000 Zuletzt bearbeitet Tue Dec 18 12:43:40 CET 2018
1000 Objekt bearb. Tue Dec 18 12:43:40 CET 2018
1000 Vgl. frl:6410740
1000 Oai Id
  1. oai:frl.publisso.de:frl:6410740 |
1000 Sichtbarkeit Metadaten public
1000 Sichtbarkeit Daten public
1000 Gegenstand von

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