Survival analysis with high-dimensional covariates
暂无分享,去创建一个
[1] Ash A. Alizadeh,et al. Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling , 2000, Nature.
[2] R. Tibshirani. Regression Shrinkage and Selection via the Lasso , 1996 .
[3] B. Nan,et al. Survival Analysis with High-Dimensional Covariates , 2010 .
[4] David E. Misek,et al. Gene-expression profiles predict survival of patients with lung adenocarcinoma , 2002, Nature Medicine.
[5] L. V. van't Veer,et al. Cross‐validated Cox regression on microarray gene expression data , 2006, Statistics in medicine.
[6] Robert Tibshirani,et al. TESTING SIGNIFICANCE OF FEATURES BY LASSOED PRINCIPAL COMPONENTS. , 2008, The annals of applied statistics.
[7] Mee Young Park,et al. L1‐regularization path algorithm for generalized linear models , 2007 .
[8] R. Tibshirani,et al. Pre-validation and inference in microarrays , 2002, Statistical applications in genetics and molecular biology.
[9] Torben Martinussen,et al. Covariate Selection for the Semiparametric Additive Risk Model , 2009 .
[10] Jeffrey T Leek,et al. The optimal discovery procedure for large-scale significance testing, with applications to comparative microarray experiments. , 2007, Biostatistics.
[11] E. Dougherty,et al. Gene-expression profiles in hereditary breast cancer. , 2001, The New England journal of medicine.
[12] R. Tibshirani,et al. Supervised harvesting of expression trees , 2001, Genome Biology.
[13] Ker-Chau Li,et al. Sliced Inverse Regression for Dimension Reduction , 1991 .
[14] Arnoldo Frigessi,et al. BIOINFORMATICS ORIGINAL PAPER doi:10.1093/bioinformatics/btm305 Gene expression Predicting survival from microarray data—a comparative study , 2022 .
[15] Yudong D. He,et al. Gene expression profiling predicts clinical outcome of breast cancer , 2002, Nature.
[16] X. Cui,et al. Statistical tests for differential expression in cDNA microarray experiments , 2003, Genome Biology.
[17] Jane-Ling Wang,et al. Dimension reduction for censored regression data , 1999 .
[18] C. Gieger,et al. Genomewide association analysis of coronary artery disease. , 2007, The New England journal of medicine.
[19] Jiang Gui,et al. Partial Cox regression analysis for high-dimensional microarray gene expression data , 2004, ISMB/ECCB.
[20] Anestis Antoniadis,et al. The Dantzig Selector in Cox's Proportional Hazards Model , 2009 .
[21] P. J. Verweij,et al. Cross-validation in survival analysis. , 1993, Statistics in medicine.
[22] Ingrid Lönnstedt. Replicated microarray data , 2001 .
[23] J. Kalbfleisch,et al. The Statistical Analysis of Failure Time Data , 1980 .
[24] John D. Storey,et al. Statistical significance for genomewide studies , 2003, Proceedings of the National Academy of Sciences of the United States of America.
[25] Danh V. Nguyen,et al. Partial least squares proportional hazard regression for application to DNA microarray survival data , 2002, Bioinform..
[26] Jiang Gui,et al. Penalized Cox regression analysis in the high-dimensional and low-sample size settings, with applications to microarray gene expression data , 2005, Bioinform..
[27] D. Allison,et al. Microarray data analysis: from disarray to consolidation and consensus , 2006, Nature Reviews Genetics.
[28] Terence Tao,et al. The Dantzig selector: Statistical estimation when P is much larger than n , 2005, math/0506081.
[29] D. Botstein,et al. Cluster analysis and display of genome-wide expression patterns. , 1998, Proceedings of the National Academy of Sciences of the United States of America.
[30] Ker-Chau Li. Sliced inverse regression for dimension reduction (with discussion) , 1991 .
[31] J. Klein,et al. Survival Analysis: Techniques for Censored and Truncated Data , 1997 .
[32] E Graf,et al. Assessment and comparison of prognostic classification schemes for survival data. , 1999, Statistics in medicine.
[33] Hongzhe Li,et al. Dimension reduction methods for microarrays with application to censored survival data , 2004, Bioinform..
[34] Robert J Tibshirani,et al. Statistical Applications in Genetics and Molecular Biology , 2011 .
[35] Uc San Francisco,et al. Microarray Gene Expression Data with Linked Survival Phenotypes: Diffuse Large-B-Cell Lymphoma Revisited , 2005 .
[36] S. Dudoit,et al. Multiple Hypothesis Testing in Microarray Experiments , 2003 .
[37] T. Lumley,et al. Time‐Dependent ROC Curves for Censored Survival Data and a Diagnostic Marker , 2000, Biometrics.
[38] Meland,et al. THE USE OF MOLECULAR PROFILING TO PREDICT SURVIVAL AFTER CHEMOTHERAPY FOR DIFFUSE LARGE-B-CELL LYMPHOMA , 2002 .
[39] W. Massy. Principal Components Regression in Exploratory Statistical Research , 1965 .
[40] R. Tibshirani,et al. Semi-Supervised Methods to Predict Patient Survival from Gene Expression Data , 2004, PLoS biology.
[41] T. Hudson,et al. A genome-wide association study identifies novel risk loci for type 2 diabetes , 2007, Nature.
[42] R. Tibshirani,et al. Significance analysis of microarrays applied to the ionizing radiation response , 2001, Proceedings of the National Academy of Sciences of the United States of America.
[43] R. Tibshirani,et al. Repeated observation of breast tumor subtypes in independent gene expression data sets , 2003, Proceedings of the National Academy of Sciences of the United States of America.
[44] D. Balding. A tutorial on statistical methods for population association studies , 2006, Nature Reviews Genetics.
[45] R. Tibshirani,et al. Covariance‐regularized regression and classification for high dimensional problems , 2009, Journal of the Royal Statistical Society. Series B, Statistical methodology.
[46] Judy H Cho,et al. Genome-wide association study identifies new susceptibility loci for Crohn disease and implicates autophagy in disease pathogenesis , 2007, Nature Genetics.
[47] Shuangge Ma,et al. Additive Risk Models for Survival Data with High‐Dimensional Covariates , 2006, Biometrics.
[48] Christian A. Rees,et al. Distinctive gene expression patterns in human mammary epithelial cells and breast cancers. , 1999, Proceedings of the National Academy of Sciences of the United States of America.
[49] X. Cui,et al. Improved statistical tests for differential gene expression by shrinking variance components estimates. , 2005, Biostatistics.
[50] P. J. Verweij,et al. Penalized likelihood in Cox regression. , 1994, Statistics in medicine.
[51] Lu Tian,et al. Linking gene expression data with patient survival times using partial least squares , 2002, ISMB.
[52] Robert Tibshirani,et al. Gene Expression Profiling Predicts Survival in Conventional Renal Cell Carcinoma , 2005, PLoS medicine.
[53] Judy H. Cho,et al. A Genome-Wide Association Study Identifies IL23R as an Inflammatory Bowel Disease Gene , 2006, Science.
[54] H. Zou,et al. Regularization and variable selection via the elastic net , 2005 .
[55] A. E. Hoerl,et al. Ridge regression: biased estimation for nonorthogonal problems , 2000 .
[56] Laurence L. George,et al. The Statistical Analysis of Failure Time Data , 2003, Technometrics.
[57] R. Tibshirani,et al. Prediction by Supervised Principal Components , 2006 .
[58] Y. Benjamini,et al. Controlling the false discovery rate: a practical and powerful approach to multiple testing , 1995 .
[59] Harald Binder,et al. Assessment of survival prediction models based on microarray data , 2007, Bioinform..
[60] M. Daly,et al. Genome-wide association studies for common diseases and complex traits , 2005, Nature Reviews Genetics.
[61] R. Tibshirani. The lasso method for variable selection in the Cox model. , 1997, Statistics in medicine.
[62] R. Tibshirani,et al. Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications , 2001, Proceedings of the National Academy of Sciences of the United States of America.
[63] Hongzhe Li,et al. Boosting proportional hazards models using smoothing splines, with applications to high-dimensional microarray data , 2005, Bioinform..
[64] J. Mesirov,et al. Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. , 1999, Science.
[65] Anne-Laure Boulesteix,et al. Partial least squares: a versatile tool for the analysis of high-dimensional genomic data , 2006, Briefings Bioinform..
[66] T. Poggio,et al. Multiclass cancer diagnosis using tumor gene expression signatures , 2001, Proceedings of the National Academy of Sciences of the United States of America.