Sparse logistic regression with a L(1/2) penalty for gene selection in cancer classification

BACKGROUND: Microarray technology is widely used in cancer diagnosis. Successfully identifying gene biomarkers will significantly help to classify different cancer types and improve the prediction accuracy. The regularization approach is one of the effective methods for gene selection in microarray...

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Detalles Bibliográficos
Autores principales: Liang, Yong, Liu, Cheng, Luan, Xin-Ze, Leung, Kwong-Sak, Chan, Tak-Ming, Xu, Zong-Ben, Zhang, Hai
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2013
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3718705/
https://www.ncbi.nlm.nih.gov/pubmed/23777239
http://dx.doi.org/10.1186/1471-2105-14-198

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3718705/
https://www.ncbi.nlm.nih.gov/pubmed/23777239
http://dx.doi.org/10.1186/1471-2105-14-198

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