Variations in the Intragene Methylation Profiles Hallmark Induced Pluripotency.
We demonstrate the potential of differentiating embryonic and induced pluripotent stem cells by the regularized linear and decision tree machine learning classification algorithms, based on a number of intragene methylation measures. The resulting average accuracy of classification has been proven t...
| Publicado en: | BioMed Research International Vol. 2015; pp. 1 - 10 |
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| Autores principales: | , , , , , , |
| Formato: | equations & formulas research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
11/5/2015
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=113630016&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 113630016 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 11/5/2015 vid: 2015 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 113630016 113630016 113630016 10.1155/2015/976362 113630016 ppf: 1 ppct: 9 formats: fmt: @attributes: type: P tig: atl: Variations in the Intragene Methylation Profiles Hallmark Induced Pluripotency. aug: au: Druzhkov, Pavel Zolotykh, Nikolay Meyerov, Iosif Alsaedi, Ahmed Shutova, Maria Ivanchenko, Mikhail Zaikin, Alexey affil: Department of Algebra, Geometry and Discrete Mathematics, Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russia sug: subj: Stem Cells Classification Algorithms DNA Methylation Human Decision Trees Phenotype ab: We demonstrate the potential of differentiating embryonic and induced pluripotent stem cells by the regularized linear and decision tree machine learning classification algorithms, based on a number of intragene methylation measures. The resulting average accuracy of classification has been proven to be above 95%, which overcomes the earlier achievements. We propose a constructive and transparent method of feature selection based on classifier accuracy. Enrichment analysis reveals statistically meaningful presence of stemness group and cancer discriminating genes among the selected best classifying features. These findings stimulate the further research on the functional consequences of these differences in methylation patterns. The presented approach can be broadly used to discriminate the cells of different phenotype or in different state by their methylation profiles, identify groups of genes constituting multifeature classifiers, and assess enrichment of these groups by the sets of genes with a functionality of interest. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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