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Learning Biomarkers of Pluripotent Stem Cells in Mouse
- Source :
- DNA research, 4(1): 233-251, DNA Research: An International Journal for Rapid Publication of Reports on Genes and Genomes
- Publication Year :
- 2011
-
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.
- Subjects :
- Pluripotent Stem Cells
Cell type
Cellular differentiation
Computational biology
Biology
Mice
feature selection
genetic algorithm
support vector machine
pluripotency
machine learning
Artificial Intelligence
Genetics
Animals
Induced pluripotent stem cell
Molecular Biology
Gene Expression Profiling
Computational Biology
Gene Expression Regulation, Developmental
Cell Differentiation
General Medicine
Full Papers
Random forest
Support vector machine
Gene expression profiling
Biomarker (medicine)
Function (biology)
Algorithms
Biomarkers
Signal Transduction
Subjects
Details
- Language :
- English
- Database :
- OpenAIRE
- Journal :
- DNA research, 4(1): 233-251, DNA Research: An International Journal for Rapid Publication of Reports on Genes and Genomes
- Accession number :
- edsair.doi.dedup.....299ddb42db9c55b852a73afd01bf6cce