IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Survival prediction using gene expression data: A review and comparison
Computational Statistics & Data Analysis
Gene identification and survival prediction with Lp Cox regression and novel similarity measure
International Journal of Data Mining and Bioinformatics
Correlation-based relevancy and redundancy measures for efficient gene selection
PRIB'07 Proceedings of the 2nd IAPR international conference on Pattern recognition in bioinformatics
Recursive Mahalanobis Separability Measure for Gene Subset Selection
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
An excellent feature selection model using gradient-based and point injection techniques
ICONIP'06 Proceedings of the 13th international conference on Neural Information Processing - Volume Part II
Iterative l1/2 regularization algorithm for variable selection in the cox proportional hazards model
ICSI'12 Proceedings of the Third international conference on Advances in Swarm Intelligence - Volume Part II
The L1/2 regularization method for variable selection in the Cox model
Applied Soft Computing
Variable selection for generalized linear mixed models by L1-penalized estimation
Statistics and Computing
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Motivation: An important application of microarray technology is to relate gene expression profiles to various clinical phenotypes of patients. Success has been demonstrated in molecular classification of cancer in which the gene expression data serve as predictors and different types of cancer serve as a categorical outcome variable. However, there has been less research in linking gene expression profiles to the censored survival data such as patients' overall survival time or time to cancer relapse. It would be desirable to have models with good prediction accuracy and parsimony property. Results: We propose to use the L1 penalized estimation for the Cox model to select genes that are relevant to patients' survival and to build a predictive model for future prediction. The computational difficulty associated with the estimation in the high-dimensional and low-sample size settings can be efficiently solved by using the recently developed least-angle regression (LARS) method. Our simulation studies and application to real datasets on predicting survival after chemotherapy for patients with diffuse large B-cell lymphoma demonstrate that the proposed procedure, which we call the LARS--Cox procedure, can be used for identifying important genes that are related to time to death due to cancer and for building a parsimonious model for predicting the survival of future patients. The LARS--Cox regression gives better predictive performance than the L2 penalized regression and a few other dimension-reduction based methods. Conclusions: We conclude that the proposed LARS--Cox procedure can be very useful in identifying genes relevant to survival phenotypes and in building a parsimonious predictive model that can be used for classifying future patients into clinically relevant high- and low-risk groups based on the gene expression profile and survival times of previous patients. Supplementary information: http://dna.ucdavis.edu/~hli/LARSCox-Appendix.pdf Contact: hli@ucdavis.edu