Gene Selection using a High-Dimensional Regression Model with Microarrays in Cancer Prognostic Studies

Gene Selection using a High-Dimensional Regression Model with Microarrays in Cancer Prognostic Studies

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Mining of gene expression data to identify genes associated with patient survival is an ongoing problem in cancer prognostic studies using microarrays in order to use such genes to achieve more accurate prognoses. The least absolute shrinkage and selection operator (lasso) is often used for gene sel...

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Bibliographic Details
Main Authors:	Shuhei Kaneko, Akihiro Hirakawa, Chikuma Hamada
Format:	Article
Language:	English
Published:	SAGE Publishing 2012-01-01
Series:	Cancer Informatics
Online Access:	https://doi.org/10.4137/CIN.S9048

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