title: Pluripotent stem cells in mice.
abstract: 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.
authors.affiliation: Institute for Biostatistics and Informatics in Medicine and Aging Research, University of Rostock, Germany. rainer.schmidt@uni-rostock.de
authors.forename: Rainer
authors.initials: R
authors.lastname: Schmidt
authors.forename: Lena
authors.initials: L
authors.lastname: Scheubert
authors.forename: Mitja
authors.initials: M
authors.lastname: Lustrek
authors.forename: Dirk
authors.initials: D
authors.lastname: Repsilber
authors.forename: Georg
authors.initials: G
authors.lastname: Fuellen
mesh_terms: D000818:Animals; D001185:Artificial Intelligence; D015415:Biomarkers / Q000032:analysis; D057225:Data Mining / Q000379:methods*; D003628:Database Management Systems; D030541:Databases, Genetic; D020869:Gene Expression Profiling / Q000379:methods*; D051379:Mice; D039904:Pluripotent Stem Cells / Q000145:classification* / Q000378:metabolism*; D020543:Proteome / Q000032:analysis*
chemical_list: D015415:Biomarkers; D020543:Proteome