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Learning gene regulatory networks from only positive and unlabeled data
BACKGROUND: Recently, supervised learning methods have been exploited to reconstruct gene regulatory networks from gene expression data. The reconstruction of a network is modeled as a binary classification problem for each pair of genes. A statistical classifier is trained to recognize the relation...
| Autores principales: | , , |
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| Formato: | Texto |
| Lenguaje: | English |
| Publicado: |
BioMed Central
2010
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2887423/ https://www.ncbi.nlm.nih.gov/pubmed/20444264 http://dx.doi.org/10.1186/1471-2105-11-228 |