Adaptive L1/2 Shooting Regularization Method for Survival Analysis Using Gene Expression Data

被引：6

作者：

Liu, Xiao-Ying ^{[1
,2
]}

Liang, Yong ^{[1
,2
]}

Xu, Zong-Ben ^{[3
]}

Zhang, Hai ^{[3
]}

Leung, Kwong-Sak ^{[4
]}

机构：

[1] Macau Univ Sci & Technol, Fac Informat Technol, Macau 999078, Peoples R China

[2] Macau Univ Sci & Technol, State Key Lab Qual Res Chinese Med, Macau 999078, Peoples R China

[3] Xi An Jiao Tong Univ, Fac Sci, Xian 710000, Peoples R China

[4] Chinese Univ Hong Kong, Dept Comp Sci & Technol, Hong Kong 999077, Hong Kong, Peoples R China

来源：

SCIENTIFIC WORLD JOURNAL | 2013年

基金：

中国国家自然科学基金;

关键词：

VARIABLE SELECTION; REGRESSION; LASSO;

D O I：

10.1155/2013/475702

中图分类号：

O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

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.

引用

页数：5

共 13 条

[1] PARTIAL LIKELIHOOD [J].

COX, DR .

BIOMETRIKA, 1975, 62 (02) :269-276

[2]

COX DR, 1972, J R STAT SOC B, V34, P187

[3]

Fan JQ, 2002, ANN STAT, V30, P74

[4] Penalized regressions: The bridge versus the lasso [J].

Fu, WJJ .

JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS, 1998, 7 (03) :397-416

[5] Penalized Cox regression analysis in the high-dimensional and low-sample size settings, with applications to microarray gene expression data [J].

Gui, J ;

Li, HZ .

BIOINFORMATICS, 2005, 21 (13) :3001-3008

[6]

Jun Qian, 2010, Proceedings of the Third International Conference on Information and Computing Science (ICIC 2010), P93, DOI 10.1109/ICIC.2010.294

[7] Sparse logistic regression with a L1/2 penalty for gene selection in cancer classification [J].

Liang, Yong ;

Liu, Cheng ;

Luan, Xin-Ze ;

Leung, Kwong-Sak ;

Chan, Tak-Ming ;

Xu, Zong-Ben ;

Zhang, Hai .

BMC BIOINFORMATICS, 2013, 14

[8] The use of molecular profiling to predict survival after chemotherapy for diffuse large-B-cell lymphoma [J].

Rosenwald, A ;

Wright, G ;

Chan, WC ;

Connors, JM ;

Campo, E ;

Fisher, RI ;

Gascoyne, RD ;

Muller-Hermelink, HK ;

Smeland, EB ;

Staudt, LM .

NEW ENGLAND JOURNAL OF MEDICINE, 2002, 346 (25) :1937-1947

[9]

Tibshirani R, 1997, STAT MED, V16, P385, DOI 10.1002/(SICI)1097-0258(19970228)16:4<385::AID-SIM380>3.0.CO

[10]

2-3

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