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Integrated biclustering of heterogeneous genome-wide datasets for the inference of global regulatory networks
BACKGROUND: 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 co-regulated groups, as opposed to those that are simply co-expres...
| Autores principales: | , , |
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| Formato: | Texto |
| Lenguaje: | English |
| Publicado: |
BioMed Central
2006
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1502140/ https://www.ncbi.nlm.nih.gov/pubmed/16749936 http://dx.doi.org/10.1186/1471-2105-7-280 |