Sparse discriminant analysis for breast cancer biomarker identification and classification

[1]  W. Pierceall,et al.  Frequent alterations in E-cadherin and alpha- and beta-catenin expression in human breast cancer cell lines. , 1995, Oncogene.

[2]  Jianqing Fan,et al.  Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties , 2001 .

[3]  Thomas A. Darden,et al.  Gene selection for sample classification based on gene expression data: study of sensitivity to choice of parameters of the GA/KNN method , 2001, Bioinform..

[4]  E. Dougherty,et al.  Gene-expression profiles in hereditary breast cancer. , 2001, The New England journal of medicine.

[5]  S. Dudoit,et al.  Comparison of Discrimination Methods for the Classification of Tumors Using Gene Expression Data , 2002 .

[6]  Yudong D. He,et al.  A Gene-Expression Signature as a Predictor of Survival in Breast Cancer , 2002 .

[7]  Bruce Randall Donald,et al.  Probabilistic Disease Classification of Expression-Dependent Proteomic Data from Mass Spectrometry of Human Serum , 2003, J. Comput. Biol..

[8]  David M. Rocke,et al.  Discriminant models for high‐throughput proteomics mass spectrometer data , 2003, Proteomics.

[9]  U. Schmidt,et al.  Cancer diagnosis and microarrays. , 2003, The international journal of biochemistry & cell biology.

[10]  Trevor Hastie,et al.  Class Prediction by Nearest Shrunken Centroids, with Applications to DNA Microarrays , 2003 .

[11]  Marina Vannucci,et al.  Gene selection: a Bayesian variable selection approach , 2003, Bioinform..

[12]  Xia Li,et al.  Gene mining: a novel and powerful ensemble decision approach to hunting for disease genes using microarray expression profiling. , 2004, Nucleic acids research.

[13]  A. Schäffer,et al.  Tumor classification using phylogenetic methods on expression data. , 2004, Journal of theoretical biology.

[14]  E. Dougherty,et al.  NONLINEAR PROBIT GENE CLASSIFICATION USING MUTUAL INFORMATION AND WAVELET-BASED FEATURE SELECTION , 2004 .

[15]  Jae Won Lee,et al.  An extensive comparison of recent classification tools applied to microarray data , 2004, Comput. Stat. Data Anal..

[16]  H. Zou,et al.  Regularization and variable selection via the elastic net , 2005 .

[17]  S. Nelson,et al.  Loss of annexin A1 expression in human breast cancer detected by multiple high-throughput analyses. , 2004, Biochemical and biophysical research communications.

[18]  D. Dai,et al.  Generalized Discriminant Analysis for Tumor Classification with Gene Expression Data , 2006, 2006 International Conference on Machine Learning and Cybernetics.

[19]  Jieping Ye,et al.  Regularized discriminant analysis for high dimensional, low sample size data , 2006, KDD '06.

[20]  Y. Hosoi,et al.  Suppression of anchorage-independent growth by expression of the ataxia-telangiectasia group D complementing gene, ATDC. , 2006, Biochemical and biophysical research communications.

[21]  Shili Lin.  Mixture modeling of progression pathways of heterogeneous breast tumors. , 2007, Journal of theoretical biology.

[22]  Wei Pan,et al.  Penalized Model-Based Clustering with Application to Variable Selection , 2007, J. Mach. Learn. Res..

[23]  Hong Yan,et al.  Feature Extraction and Uncorrelated Discriminant Analysis for High-Dimensional Data , 2008, IEEE Transactions on Knowledge and Data Engineering.

[24]  Melanie Hilario,et al.  Approaches to dimensionality reduction in proteomic biomarker studies , 2007, Briefings Bioinform..

[25]  Renee F Wilson,et al.  Systematic Review: Gene Expression Profiling Assays in Early-Stage Breast Cancer , 2008, Annals of Internal Medicine.

[26]  Xia Li,et al.  A feature ensemble technology to identify molecular mechanisms for distinction between multiple subtypes of lymphoma , 2008 .