Gene Selection for Microarray Data
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[1] Thomas Marill,et al. On the effectiveness of receptors in recognition systems , 1963, IEEE Trans. Inf. Theory.
[2] R. Bellman,et al. V. Adaptive Control Processes , 1964 .
[3] John W. Tukey,et al. A Projection Pursuit Algorithm for Exploratory Data Analysis , 1974, IEEE Transactions on Computers.
[4] C. J. Stone,et al. Optimal Rates of Convergence for Nonparametric Estimators , 1980 .
[5] J. Friedman,et al. Projection Pursuit Regression , 1981 .
[6] E. Oja. Simplified neuron model as a principal component analyzer , 1982, Journal of mathematical biology.
[7] Leo Breiman,et al. Classification and Regression Trees , 1984 .
[8] D. Lipman,et al. Rapid and sensitive protein similarity searches. , 1985, Science.
[9] K. Khrapko,et al. [Determination of the nucleotide sequence of DNA using hybridization with oligonucleotides. A new method]. , 1988, Doklady Akademii nauk SSSR.
[10] W. Bains,et al. A novel method for nucleic acid sequence determination. , 1988, Journal of theoretical biology.
[11] Lei Xu,et al. Best first strategy for feature selection , 1988, [1988 Proceedings] 9th International Conference on Pattern Recognition.
[12] Jack Sklansky,et al. On Automatic Feature Selection , 1988, Int. J. Pattern Recognit. Artif. Intell..
[13] R. Drmanac,et al. Sequencing of megabase plus DNA by hybridization: theory of the method. , 1989, Genomics.
[14] Yann LeCun,et al. Optimal Brain Damage , 1989, NIPS.
[15] Christian Jutten,et al. Blind separation of sources, part I: An adaptive algorithm based on neuromimetic architecture , 1991, Signal Process..
[16] Larry A. Rendell,et al. A Practical Approach to Feature Selection , 1992, ML.
[17] Kenneth A. De Jong,et al. Genetic algorithms as a tool for feature selection in machine learning , 1992, Proceedings Fourth International Conference on Tools with Artificial Intelligence TAI '92.
[18] J. Edward Jackson,et al. A User's Guide to Principal Components. , 1991 .
[19] Babak Hassibi,et al. Second Order Derivatives for Network Pruning: Optimal Brain Surgeon , 1992, NIPS.
[20] Larry A. Rendell,et al. The Feature Selection Problem: Traditional Methods and a New Algorithm , 1992, AAAI.
[21] J. Cardoso,et al. Blind beamforming for non-gaussian signals , 1993 .
[22] Claire Cardie,et al. Using Decision Trees to Improve Case-Based Learning , 1993, ICML.
[23] Peter D. Turney. Exploiting Context When Learning to Classify , 1993, ECML.
[24] Kenneth DeJong,et al. Robust feature selection algorithms , 1993, Proceedings of 1993 IEEE Conference on Tools with Al (TAI-93).
[25] David B. Skalak,et al. Prototype and Feature Selection by Sampling and Random Mutation Hill Climbing Algorithms , 1994, ICML.
[26] S. Elgin,et al. Nucleosome positioning and gene regulation , 1994, Journal of cellular biochemistry.
[27] P. Langley. Selection of Relevant Features in Machine Learning , 1994 .
[28] Igor Kononenko,et al. Estimating Attributes: Analysis and Extensions of RELIEF , 1994, ECML.
[29] Ron Kohavi,et al. Irrelevant Features and the Subset Selection Problem , 1994, ICML.
[30] Rich Caruana,et al. Greedy Attribute Selection , 1994, ICML.
[31] Terrence J. Sejnowski,et al. An Information-Maximization Approach to Blind Separation and Blind Deconvolution , 1995, Neural Computation.
[32] Daphne Koller,et al. Toward Optimal Feature Selection , 1996, ICML.
[33] John Shawe-Taylor,et al. A framework for structural risk minimisation , 1996, COLT '96.
[34] Huan Liu,et al. A Probabilistic Approach to Feature Selection - A Filter Solution , 1996, ICML.
[35] Ron Kohavi,et al. Wrappers for Feature Subset Selection , 1997, Artif. Intell..
[36] Pat Langley,et al. Selection of Relevant Features and Examples in Machine Learning , 1997, Artif. Intell..
[37] Marko Robnik-Sikonja,et al. An adaptation of Relief for attribute estimation in regression , 1997, ICML.
[38] Paul S. Bradley,et al. Feature Selection via Concave Minimization and Support Vector Machines , 1998, ICML.
[39] Vladimir Vapnik,et al. Statistical learning theory , 1998 .
[40] Bernhard Schölkopf,et al. Semiparametric Support Vector and Linear Programming Machines , 1998, NIPS.
[41] C. Nusbaum,et al. Large-scale identification, mapping, and genotyping of single-nucleotide polymorphisms in the human genome. , 1998, Science.
[42] J. Mesirov,et al. Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. , 1999, Science.
[43] S. Hochreiter,et al. Lococode Performs Nonlinear ICA Without Knowing The Number Of Sources , 1999 .
[44] D. Gerhold,et al. DNA chips: promising toys have become powerful tools. , 1999, Trends in biochemical sciences.
[45] Laurie J. Heyer,et al. Exploring expression data: identification and analysis of coexpressed genes. , 1999, Genome research.
[46] Andrea Califano,et al. Analysis of Gene Expression Microarrays for Phenotype Classification , 2000, ISMB.
[47] Sayan Mukherjee,et al. Feature Selection for SVMs , 2000, NIPS.
[48] Nello Cristianini,et al. Support vector machine classification and validation of cancer tissue samples using microarray expression data , 2000, Bioinform..
[49] Gary A. Churchill,et al. Analysis of Variance for Gene Expression Microarray Data , 2000, J. Comput. Biol..
[50] E. Wolski,et al. Normalization strategies for cDNA microarrays. , 2000, Nucleic acids research.
[51] Naftali Tishby,et al. The information bottleneck method , 2000, ArXiv.
[52] D. Botstein,et al. A gene expression database for the molecular pharmacology of cancer , 2000, Nature Genetics.
[53] 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.
[54] P S Meltzer,et al. Gastrointestinal stromal tumors with KIT mutations exhibit a remarkably homogeneous gene expression profile. , 2001, Cancer research.
[55] Trevor Hastie,et al. The Elements of Statistical Learning , 2001 .
[56] E. Oja,et al. Independent Component Analysis , 2013 .
[57] Tommi S. Jaakkola,et al. Maximum-likelihood estimation of optimal scaling factors for expression array normalization , 2001, SPIE BiOS.
[58] Sanmay Das,et al. Filters, Wrappers and a Boosting-Based Hybrid for Feature Selection , 2001, ICML.
[59] M. Oh,et al. Issues in cDNA microarray analysis: quality filtering, channel normalization, models of variations and assessment of gene effects. , 2001, Nucleic acids research.
[60] D. Slonim,et al. Evaluation of normalization procedures for oligonucleotide array data based on spiked cRNA controls , 2001, Genome Biology.
[61] Amos Bairoch,et al. The PROSITE database, its status in 2002 , 2002, Nucleic Acids Res..
[62] Amos Bairoch,et al. PROSITE: A Documented Database Using Patterns and Profiles as Motif Descriptors , 2002, Briefings Bioinform..
[63] Yudong D. He,et al. Gene expression profiling predicts clinical outcome of breast cancer , 2002, Nature.
[64] T. Poggio,et al. Prediction of central nervous system embryonal tumour outcome based on gene expression , 2002, Nature.
[65] S. Dudoit,et al. Normalization for cDNA microarray data: a robust composite method addressing single and multiple slide systematic variation. , 2002, Nucleic acids research.
[66] I. Jolliffe. Principal Component Analysis , 2002 .
[67] Todd,et al. Diffuse large B-cell lymphoma outcome prediction by gene-expression profiling and supervised machine learning , 2002, Nature Medicine.
[68] Martin Vingron,et al. Variance stabilization applied to microarray data calibration and to the quantification of differential expression , 2002, ISMB.
[69] Klaus Obermayer,et al. Feature Selection and Classification on Matrix Data: From Large Margins to Small Covering Numbers , 2002, NIPS.
[70] Peter D. Turney. Robust Classification with Context-Sensitive Features , 2002, ArXiv.
[71] Douglas M. Hawkins,et al. A variance-stabilizing transformation for gene-expression microarray data , 2002, ISMB.
[72] Alain Rakotomamonjy,et al. Variable Selection Using SVM-based Criteria , 2003, J. Mach. Learn. Res..
[73] Bernhard Schölkopf,et al. Use of the Zero-Norm with Linear Models and Kernel Methods , 2003, J. Mach. Learn. Res..
[74] James Theiler,et al. Grafting: Fast, Incremental Feature Selection by Gradient Descent in Function Space , 2003, J. Mach. Learn. Res..
[75] Isabelle Guyon,et al. An Introduction to Variable and Feature Selection , 2003, J. Mach. Learn. Res..
[76] Jinbo Bi,et al. Dimensionality Reduction via Sparse Support Vector Machines , 2003, J. Mach. Learn. Res..
[77] Walter L. Ruzzo,et al. Improved Gene Selection for Classification of Microarrays , 2002, Pacific Symposium on Biocomputing.
[78] Jerome H. Friedman,et al. On Bias, Variance, 0/1—Loss, and the Curse-of-Dimensionality , 2004, Data Mining and Knowledge Discovery.
[79] Nello Cristianini,et al. Latent Semantic Kernels , 2001, Journal of Intelligent Information Systems.
[80] J. Ross Quinlan,et al. Induction of Decision Trees , 1986, Machine Learning.
[81] Jason Weston,et al. Gene Selection for Cancer Classification using Support Vector Machines , 2002, Machine Learning.