title: Variations in the Intragene Methylation Profiles Hallmark Induced Pluripotency.
abstract: 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.
authors.affiliation: Department of Algebra, Geometry and Discrete Mathematics, Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russia.
authors.forename: Pavel
authors.initials: P
authors.lastname: Druzhkov
authors.affiliation: Department of Algebra, Geometry and Discrete Mathematics, Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russia.
authors.forename: Nikolay
authors.initials: N
authors.lastname: Zolotykh
authors.affiliation: Department of Mathematical Software and High-Performance Computing, Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russia.
authors.forename: Iosif
authors.initials: I
authors.lastname: Meyerov
authors.affiliation: Department of Mathematics, King Abdulaziz University, Jeddah, Saudi Arabia.
authors.forename: Ahmed
authors.initials: A
authors.lastname: Alsaedi
authors.affiliation: Stem Cell Laboratory, Vavilov Institute of General Genetics, RAS, Moscow, Russia.
authors.forename: Maria
authors.initials: M
authors.lastname: Shutova
authors.affiliation: Department of Applied Mathematics and Center of Bioinformatics, Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russia.
authors.forename: Mikhail
authors.initials: M
authors.lastname: Ivanchenko
authors.affiliation: Department of Applied Mathematics and Center of Bioinformatics, Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russia ; Institute for Women's Health and Department of Mathematics, University College London, London, UK.
authors.forename: Alexey
authors.initials: A
authors.lastname: Zaikin
mesh_terms: D000465:Algorithms; D003663:Decision Trees; D053595:Embryonic Stem Cells / Q000502:physiology*; D006801:Humans; D057026:Induced Pluripotent Stem Cells / Q000502:physiology*; D008745:Methylation