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The Scientific World Journal
Volume 2013, Article ID 475702, 5 pages
Research Article

Adaptive Shooting Regularization Method for Survival Analysis Using Gene Expression Data

1Faculty of Information Technology & State Key Laboratory of Quality Research in Chinese Medicines, Macau University of Science and Technology, Macau 999078, China
2Faculty of Science, Xi’an Jiaotong University, Xi’an 710000, China
3Department of Computer Science and Technology, The Chinese University of Hong Kong, Hong Kong 999077, China

Received 3 September 2013; Accepted 30 October 2013

Academic Editors: J. Ma, B. Shen, J. Wang, and J. Wang

Copyright © 2013 Xiao-Ying Liu et al. 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.


A new adaptive shooting regularization method for variable selection based on the Cox’s proportional hazards mode being proposed. This adaptive shooting algorithm can be easily obtained by the optimization of a reweighed iterative series of penalties and a shooting strategy of penalty. Simulation results based on high dimensional artificial data show that the adaptive 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 regularization method performs competitively.