Using the information embedded in the testing sample to break the limits caused by the small sample size in microarray-based classification
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[1] R. Fisher. THE STATISTICAL UTILIZATION OF MULTIPLE MEASUREMENTS , 1938 .
[2] B. Efron. The jackknife, the bootstrap, and other resampling plans , 1987 .
[3] Bernhard E. Boser,et al. A training algorithm for optimal margin classifiers , 1992, COLT '92.
[4] László Györfi,et al. A Probabilistic Theory of Pattern Recognition , 1996, Stochastic Modelling and Applied Probability.
[5] 中澤 真,et al. Devroye, L., Gyorfi, L. and Lugosi, G. : A Probabilistic Theory of Pattern Recognition, Springer (1996). , 1997 .
[6] Vladimir Vapnik,et al. Statistical learning theory , 1998 .
[7] J. Mesirov,et al. Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. , 1999, Science.
[8] 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.
[9] E. Boerwinkle,et al. Feature (gene) selection in gene expression-based tumor classification. , 2001, Molecular genetics and metabolism.
[10] Avinash C. Kak,et al. PCA versus LDA , 2001, IEEE Trans. Pattern Anal. Mach. Intell..
[11] Yudong D. He,et al. Gene expression profiling predicts clinical outcome of breast cancer , 2002, Nature.
[12] T. Poggio,et al. Prediction of central nervous system embryonal tumour outcome based on gene expression , 2002, Nature.
[13] Todd,et al. Diffuse large B-cell lymphoma outcome prediction by gene-expression profiling and supervised machine learning , 2002, Nature Medicine.
[14] S. Dudoit,et al. Comparison of Discrimination Methods for the Classification of Tumors Using Gene Expression Data , 2002 .
[15] E. Lander,et al. Gene expression correlates of clinical prostate cancer behavior. , 2002, Cancer cell.
[16] J. Ioannidis,et al. Predictive ability of DNA microarrays for cancer outcomes and correlates: an empirical assessment , 2003, The Lancet.
[17] Richard M. Simon,et al. A Paradigm for Class Prediction Using Gene Expression Profiles , 2003, J. Comput. Biol..
[18] T. Poggio,et al. General conditions for predictivity in learning theory , 2004, Nature.
[19] D. Ransohoff. Rules of evidence for cancer molecular-marker discovery and validation , 2004, Nature Reviews Cancer.
[20] P. Brown,et al. Large-scale meta-analysis of cancer microarray data identifies common transcriptional profiles of neoplastic transformation and progression. , 2004, Proceedings of the National Academy of Sciences of the United States of America.
[21] Stefan Michiels,et al. Prediction of cancer outcome with microarrays: a multiple random validation strategy , 2005, The Lancet.
[22] BMC Bioinformatics , 2005 .
[23] Zixiang Xiong,et al. Optimal number of features as a function of sample size for various classification rules , 2005, Bioinform..
[24] Aleix M. Martínez,et al. Where are linear feature extraction methods applicable? , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[25] A. Olshen,et al. Differential exoprotease activities confer tumor-specific serum peptidome patterns. , 2005, The Journal of clinical investigation.
[26] Caroline Truntzer,et al. Importance of data structure in comparing two dimension reduction methods for classification of microarray gene expression data , 2007, BMC Bioinformatics.
[27] D. Allison,et al. Microarray data analysis: from disarray to consolidation and consensus , 2006, Nature Reviews Genetics.
[28] L. Ein-Dor,et al. Thousands of samples are needed to generate a robust gene list for predicting outcome in cancer. , 2006, Proceedings of the National Academy of Sciences of the United States of America.
[29] Gavin D. Grant,et al. Common markers of proliferation , 2006, Nature Reviews Cancer.
[30] R. Nadon,et al. Inferential literacy for experimental high-throughput biology. , 2006, Trends in genetics : TIG.
[31] M. Vijver,et al. Technology Insight: tuning into the genetic orchestra using microarrays—limitations of DNA microarrays in clinical practice , 2006, Nature Clinical Practice Oncology.
[32] Aleix M. Martínez,et al. Subclass discriminant analysis , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[33] Min Zhang,et al. Variable selection for large p small n regression models with incomplete data: Mapping QTL with epistases , 2007, BMC Bioinformatics.
[34] Aleix M. Martínez,et al. Spherical-Homoscedastic Distributions: The Equivalency of Spherical and Normal Distributions in Classification , 2007, J. Mach. Learn. Res..
[35] Mark R. Viant,et al. Improved classification accuracy in 1- and 2-dimensional NMR metabolomics data using the variance stabilising generalised logarithm transformation , 2007, BMC Bioinformatics.
[36] Pieter Abbeel,et al. Max-margin Classification of Data with Absent Features , 2008, J. Mach. Learn. Res..