Statistical Applications in Genetics and Molecular Biology Sparse Logistic Regression with Lp Penalty for Biomarker Identification
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G. Tian | Zhenqiu Liu | Feng Jiang | Suna Wang | F. Sato | S. Meltzer | M. Tan
[1] J. Friedman,et al. A Statistical View of Some Chemometrics Regression Tools , 1993 .
[2] R. Tibshirani. Regression Shrinkage and Selection via the Lasso , 1996 .
[3] R. Tibshirani. The lasso method for variable selection in the Cox model. , 1997, Statistics in medicine.
[4] Paul S. Bradley,et al. Feature Selection via Concave Minimization and Support Vector Machines , 1998, ICML.
[5] Ron Kohavi,et al. The Wrapper Approach , 1998 .
[6] Michael A. Saunders,et al. Atomic Decomposition by Basis Pursuit , 1998, SIAM J. Sci. Comput..
[7] U. Alon,et al. Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays. , 1999, Proceedings of the National Academy of Sciences of the United States of America.
[8] Gary A. Churchill,et al. Analysis of Variance for Gene Expression Microarray Data , 2000, J. Comput. Biol..
[9] Wenjiang J. Fu,et al. Asymptotics for lasso-type estimators , 2000 .
[10] Vladimir N. Vapnik,et al. The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.
[11] Gérard Dreyfus,et al. Withdrawing an example from the training set: An analytic estimation of its effect on a non-linear parameterised model , 2000, Neurocomputing.
[12] Trevor Hastie,et al. The Elements of Statistical Learning , 2001 .
[13] Jianqing Fan,et al. Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties , 2001 .
[14] Christina Kendziorski,et al. On Differential Variability of Expression Ratios: Improving Statistical Inference about Gene Expression Changes from Microarray Data , 2001, J. Comput. Biol..
[15] I. Mian,et al. Identifying marker genes in transcription profiling data using a mixture of feature relevance experts. , 2001, Physiological genomics.
[16] George Eastman House,et al. Sparse Bayesian Learning and the Relevance Vector Machine , 2001 .
[17] T. H. Bø,et al. New feature subset selection procedures for classification of expression profiles , 2002, Genome Biology.
[18] A D Long,et al. Improved Statistical Inference from DNA Microarray Data Using Analysis of Variance and A Bayesian Statistical Framework , 2001, The Journal of Biological Chemistry.
[19] William Stafford Noble,et al. Analysis of strain and regional variation in gene expression in mouse brain , 2001, Genome Biology.
[20] P. Laird,et al. Hierarchical clustering of lung cancer cell lines using DNA methylation markers. , 2002, Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology.
[21] Iñaki Inza,et al. Gene selection by sequential search wrapper approaches in microarray cancer class prediction , 2002, J. Intell. Fuzzy Syst..
[22] Geoffrey J McLachlan,et al. Selection bias in gene extraction on the basis of microarray gene-expression data , 2002, Proceedings of the National Academy of Sciences of the United States of America.
[23] D. Donoho,et al. Maximal Sparsity Representation via l 1 Minimization , 2002 .
[24] Léon Personnaz,et al. MLPs (Mono-Layer Polynomials and Multi-Layer Perceptrons) for Nonlinear Modeling , 2003, J. Mach. Learn. Res..
[25] S. Sathiya Keerthi,et al. A simple and efficient algorithm for gene selection using sparse logistic regression , 2003, Bioinform..
[26] C. Ling,et al. AUC: a Statistically Consistent and more Discriminating Measure than Accuracy , 2003, IJCAI.
[27] Kam D. Dahlquist,et al. Regression Approaches for Microarray Data Analysis , 2002, J. Comput. Biol..
[28] Peter W. Laird,et al. A comparison of cluster analysis methods using DNA methylation data , 2004, Bioinform..
[29] Dmitry M. Malioutov,et al. A sparse signal reconstruction perspective for source localization with sensor arrays , 2005, IEEE Transactions on Signal Processing.
[30] Jiangsheng Yu,et al. Bayesian neural network approaches to ovarian cancer identification from high-resolution mass spectrometry data , 2005, ISMB.
[31] Jian Liu,et al. Logistic support vector machines and their application to gene expression data , 2005, Int. J. Bioinform. Res. Appl..
[32] Lawrence Carin,et al. Sparse multinomial logistic regression: fast algorithms and generalization bounds , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[33] H. Zou,et al. Regularization and variable selection via the elastic net , 2005 .
[34] Yan Xu,et al. Transcriptional profiling suggests that Barrett's metaplasia is an early intermediate stage in esophageal adenocarcinogenesis , 2006, Oncogene.
[35] Xiaodong Lin,et al. Gene expression Gene selection using support vector machines with non-convex penalty , 2005 .
[36] Tom Fawcett,et al. ROC Graphs: Notes and Practical Considerations for Researchers , 2007 .