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A Bayesian approach for inducing sparsity in generalized linear models with multi-category response
BACKGROUND: The dimension and complexity of high-throughput gene expression data create many challenges for downstream analysis. Several approaches exist to reduce the number of variables with respect to small sample sizes. In this study, we utilized the Generalized Double Pareto (GDP) prior to indu...
Tallennettuna:
| Julkaisussa: | BMC Bioinformatics |
|---|---|
| Päätekijät: | , , , , |
| Aineistotyyppi: | Artigo |
| Kieli: | Inglês |
| Julkaistu: |
BioMed Central
2015
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| Aiheet: | |
| Linkit: | https://ncbi.nlm.nih.gov/pmc/articles/PMC4597416/ https://ncbi.nlm.nih.gov/pubmed/26423345 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1186/1471-2105-16-S13-S13 |
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