Unknown

Dataset Information

0

Learning biomarkers of pluripotent stem cells in mouse.


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. 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.

SUBMITTER: Scheubert L 

PROVIDER: S-EPMC3158465 | biostudies-literature | 2011 Aug

REPOSITORIES: biostudies-literature

altmetric image

Publications

Learning biomarkers of pluripotent stem cells in mouse.

Scheubert Lena L   Schmidt Rainer R   Repsilber Dirk D   Lustrek Mitja M   Fuellen Georg G  

DNA research : an international journal for rapid publication of reports on genes and genomes 20110726 4


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  ...[more]

Similar Datasets

| S-EPMC9860076 | biostudies-literature
| S-EPMC3516428 | biostudies-literature
| S-EPMC7016803 | biostudies-literature
| S-EPMC3325228 | biostudies-literature
| S-EPMC6242311 | biostudies-literature
| S-EPMC7531137 | biostudies-literature
| S-EPMC4005486 | biostudies-literature
| S-EPMC5867603 | biostudies-literature
| S-EPMC5524993 | biostudies-literature
| S-EPMC3700107 | biostudies-literature