A novel combined ICA and clustering technique for the classification of gene expression data
This study presents an effective method of blindly classifying large amounts of gene expression data into biologically meaningful groups using a combination of independent component analysis (ICA) and clustering techniques. Specifically, we show that the genes can be classified blindly into several...
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| Main Authors: | , , |
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| Format: | Default Conference proceeding |
| Published: |
2005
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| Subjects: | |
| Online Access: | https://hdl.handle.net/2134/5665 |
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