Integrated biclustering of heterogeneous genome-wide datasets for the inference of global regulatory networks
<p>Abstract</p> <p>Background</p> <p>The learning of global genetic regulatory networks from expression data is a severely under-constrained problem that is aided by reducing the dimensionality of the search space by means of clustering genes into putatively <it>co-regulated </it>groups, as opposed...
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| Principais autores: | , , |
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| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado em: |
BMC
2006-06-01
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| coleção: | BMC Bioinformatics |
| Acesso em linha: | http://www.biomedcentral.com/1471-2105/7/280 |
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