Bayesian Hyper-LASSO Classification for Feature Selection with Application to Endometrial Cancer RNA-seq Data

Feature selection is demanded in many modern scientific research problems that use high-dimensional data. A typical example is to identify gene signatures that are related to a certain disease from high-dimensional gene expression data. The expression of genes may have grouping structures, for examp...

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Detalles Bibliográficos
Autores principales: Jiang, Lai, Greenwood, Celia M. T., Yao, Weixin, Li, Longhai
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2020
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7297975/
https://www.ncbi.nlm.nih.gov/pubmed/32546735
http://dx.doi.org/10.1038/s41598-020-66466-z

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7297975/
https://www.ncbi.nlm.nih.gov/pubmed/32546735
http://dx.doi.org/10.1038/s41598-020-66466-z

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