A signal-to-noise classification model for identification of differentially expressed genes from gene expression data
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[1] P. Broberg. Statistical methods for ranking differentially expressed genes , 2003, Genome Biology.
[2] Yukyee Leung,et al. A Multiple-Filter-Multiple-Wrapper Approach to Gene Selection and Microarray Data Classification , 2010, IEEE/ACM Transactions on Computational Biology and Bioinformatics.
[3] Ana Carolina Lorena,et al. On the Complexity of Gene Expression Classification Data Sets , 2008, 2008 Eighth International Conference on Hybrid Intelligent Systems.
[4] J. Mesirov,et al. Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. , 1999, Science.
[5] Isabelle Guyon,et al. An Introduction to Variable and Feature Selection , 2003, J. Mach. Learn. Res..
[6] Insuk Sohn,et al. Informative transcription factor selection using support vector machine-based generalized approximate cross validation criteria , 2009, Comput. Stat. Data Anal..
[7] Insuk Sohn,et al. Selecting marker genes for cancer classification using supervised weighted kernel clustering and the support vector machine , 2009, Comput. Stat. Data Anal..
[8] M. Schummer,et al. Selecting Differentially Expressed Genes from Microarray Experiments , 2003, Biometrics.
[9] Jing Shen,et al. A Novel Discrete Particle Swarm Optimization Algorithm for Microarray Data-Based Tumor Marker Gene Selection , 2008, 2008 International Conference on Computer Science and Software Engineering.
[10] A.K.C. Wong,et al. Attribute clustering for grouping, selection, and classification of gene expression data , 2005, IEEE/ACM Transactions on Computational Biology and Bioinformatics.
[11] M. Čuperlović-Culf,et al. Determination of tumour marker genes from gene expression data. , 2005, Drug discovery today.
[12] Chenn-Jung Huang,et al. A comparative study of feature selection methods for probabilistic neural networks in cancer classification , 2003, Proceedings. 15th IEEE International Conference on Tools with Artificial Intelligence.
[13] Ajith Abraham,et al. Computational Intelligence in Solving Bioinformatics Problems: Reviews, Perspectives, and Challenges , 2008, Computational Intelligence in Biomedicine and Bioinformatics.
[14] Jagath C. Rajapakse,et al. Gene and sample selection for cancer classification with support vectors based t-statistic , 2010, Neurocomputing.
[15] Prabhas Chongstitvatana,et al. Selecting Informative Genes from Microarray Data for Cancer Classification with Genetic Programming Classifier Using K-Means Clustering and SNR Ranking , 2007, 2007 Frontiers in the Convergence of Bioscience and Information Technologies.
[16] Nello Cristianini,et al. An Introduction to Support Vector Machines and Other Kernel-based Learning Methods , 2000 .
[17] Chen-An Tsai,et al. Testing for differentially expressed genes with microarray data. , 2003, Nucleic acids research.