Adaptive L(1/2) Shooting Regularization Method for Survival Analysis Using Gene Expression Data

A new adaptive L(1/2) shooting regularization method for variable selection based on the Cox's proportional hazards mode being proposed. This adaptive L(1/2) shooting algorithm can be easily obtained by the optimization of a reweighed iterative series of L(1) penalties and a shooting strategy o...

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Detalles Bibliográficos
Autores principales: Liu, Xiao-Ying, Liang, Yong, Xu, Zong-Ben, Zhang, Hai, Leung, Kwong-Sak
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi Publishing Corporation 2013
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3876878/
https://www.ncbi.nlm.nih.gov/pubmed/24453861
http://dx.doi.org/10.1155/2013/475702
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author Liu, Xiao-Ying
Liang, Yong
Xu, Zong-Ben
Zhang, Hai
Leung, Kwong-Sak
author_facet Liu, Xiao-Ying
Liang, Yong
Xu, Zong-Ben
Zhang, Hai
Leung, Kwong-Sak
author_sort Liu, Xiao-Ying
collection PubMed
description A new adaptive L(1/2) shooting regularization method for variable selection based on the Cox's proportional hazards mode being proposed. This adaptive L(1/2) shooting algorithm can be easily obtained by the optimization of a reweighed iterative series of L(1) penalties and a shooting strategy of L(1/2) penalty. Simulation results based on high dimensional artificial data show that the adaptive L(1/2) shooting regularization method can be more accurate for variable selection than Lasso and adaptive Lasso methods. The results from real gene expression dataset (DLBCL) also indicate that the L(1/2) regularization method performs competitively.
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spelling pubmed-38768782014-01-16 Adaptive L(1/2) Shooting Regularization Method for Survival Analysis Using Gene Expression Data Liu, Xiao-Ying Liang, Yong Xu, Zong-Ben Zhang, Hai Leung, Kwong-Sak ScientificWorldJournal Research Article A new adaptive L(1/2) shooting regularization method for variable selection based on the Cox's proportional hazards mode being proposed. This adaptive L(1/2) shooting algorithm can be easily obtained by the optimization of a reweighed iterative series of L(1) penalties and a shooting strategy of L(1/2) penalty. Simulation results based on high dimensional artificial data show that the adaptive L(1/2) shooting regularization method can be more accurate for variable selection than Lasso and adaptive Lasso methods. The results from real gene expression dataset (DLBCL) also indicate that the L(1/2) regularization method performs competitively. Hindawi Publishing Corporation 2013-12-15 /pmc/articles/PMC3876878/ /pubmed/24453861 http://dx.doi.org/10.1155/2013/475702 Text en Copyright © 2013 Xiao-Ying Liu et al. https://creativecommons.org/licenses/by/3.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Liu, Xiao-Ying
Liang, Yong
Xu, Zong-Ben
Zhang, Hai
Leung, Kwong-Sak
Adaptive L(1/2) Shooting Regularization Method for Survival Analysis Using Gene Expression Data
title Adaptive L(1/2) Shooting Regularization Method for Survival Analysis Using Gene Expression Data
title_full Adaptive L(1/2) Shooting Regularization Method for Survival Analysis Using Gene Expression Data
title_fullStr Adaptive L(1/2) Shooting Regularization Method for Survival Analysis Using Gene Expression Data
title_full_unstemmed Adaptive L(1/2) Shooting Regularization Method for Survival Analysis Using Gene Expression Data
title_short Adaptive L(1/2) Shooting Regularization Method for Survival Analysis Using Gene Expression Data
title_sort adaptive l(1/2) shooting regularization method for survival analysis using gene expression data
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3876878/
https://www.ncbi.nlm.nih.gov/pubmed/24453861
http://dx.doi.org/10.1155/2013/475702
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AT zhanghai adaptivel12shootingregularizationmethodforsurvivalanalysisusinggeneexpressiondata
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