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Sparse Bayesian classification and feature selection for biological expression data with high correlations
Classification models built on biological expression data are increasingly used to predict distinct disease subtypes. Selected features that separate sample groups can be the candidates of biomarkers, helping us to discover biological functions/pathways. However, three challenges are associated with...
| Autores principales: | , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Public Library of Science
2017
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5744982/ https://www.ncbi.nlm.nih.gov/pubmed/29281700 http://dx.doi.org/10.1371/journal.pone.0189541 |