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Genomics Inform > Volume 5(1); 2007 > Article
Detection of Neural Fates from Random Differentiation: Application of Support Vector MachineMin.
Min Su Lee, Jeong Hyuck Ahn, Woong Yang Park
1Department of Computer Science and Engineering, Ewha Womans University, Seoul 120-759, Korea.
2Laboratory of Molecular and Cellular Neuroscience, Rockefeller University, New York, U.S.A.
3Human Genome Research Institute and Department of Biochemistry, Seoul National University College of Medicine, Seoul 110-799, Korea.
Embryonic stem cells can be differentiated into various types of cells, requiring a tight regulation of transcription. Biomarkers related to each lineage of cells are used to guide the differentiation into neural or any other fates. In previous experiments, we reported the guided differentiation (GD)-specific genes by comparing profiles of random differentiation (RD). Interestingly 68% of differentially expressed genes in GD overlap with that of RD, which makes it difficult for us to separate the lineages by examining several markers. In this paper, we design a prediction model to identify the differentiation into neural fates from any other lineage. From the profiles of 11,376 genes, 203 differentially expressed genes between neural and random differentiation were selected by random variance T-test with 95% confidence and 5% false discovery rate. Based on support vector machine algorithm, we could select 79 marker genes from the 203 informative genes to construct the optimal prediction model. Here we propose a prediction model for the prediction of neural fates from random differentiation which is constructed with a perfect accuracy.
Keywords: stem cells; microarray; neural differentiation; random differentiation


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