Integrated biclustering of heterogeneous genome-wide datasets for the inference of global regulatory networks [electronic resource]
Background: The learning of global genetic regulatory networks from expression data is aseverely under-constrained problem that is aided by reducing the dimensionality of the searchspace by means of clustering genes into putatively co-regulated groups, as opposed to those that aresimply co-expressed...
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| Format: | Electronic eBook |
| Language: | English |
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Washington, D.C. : Oak Ridge, Tenn. :
United States. Department of Energy. Office of Science ; Distributed by the Office of Scientific and Technical Information, U.S. Department of Energy,
2006.
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