Kernel based methods for accelerated failure time model with ultra-high dimensional data

BACKGROUND: Most genomic data have ultra-high dimensions with more than 10,000 genes (probes). Regularization methods with L(1 )and L(p )penalty have been extensively studied in survival analysis with high-dimensional genomic data. However, when the sample size n ≪ m (the number of genes), directly...

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Detalles Bibliográficos
Autores principales: Liu, Zhenqiu, Chen, Dechang, Tan, Ming, Jiang, Feng, Gartenhaus, Ronald B
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2010
Materias:
Methodology Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3019227/
https://www.ncbi.nlm.nih.gov/pubmed/21176134
http://dx.doi.org/10.1186/1471-2105-11-606

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3019227/
https://www.ncbi.nlm.nih.gov/pubmed/21176134
http://dx.doi.org/10.1186/1471-2105-11-606

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