Fuzzy Logic for Elimination of Redundant Information of Microarray Data
Tài liệu tham khảo
Alon, 1999, Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays, Proc. Natl. Acad. Sci. USA, 96, 6745, 10.1073/pnas.96.12.6745
Ben-Dor, 2000, Tissue classification with gene expression profiles, J. Comput. Biol., 7, 559, 10.1089/106652700750050943
Alizadeh, 2000, Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling, Nature, 403, 503, 10.1038/35000501
Golub, 1999, Molecular classification of cancer: class discovery and class prediction by gene expression monitoring, Science, 286, 531, 10.1126/science.286.5439.531
Eisen, 1998, Cluster analysis and display of genome-wide expression patterns, Proc. Natl. Acad. Sci. USA, 95, 14863, 10.1073/pnas.95.25.14863
Xiong, 2001, Biomarker identification by feature wrappers, Genome Res., 11, 1878, 10.1101/gr.190001
Jaeger, 2003, Improved gene selection for classification of microarrays, Pac. Symp. Biocomput., 53
Yu, 2004, Redundancy based feature selection for microarray data, 737
Marohnic, 2004, Mutual information based reduction of data mining dimensionality in gene expression analysis, 1, 249
Ding, 2005, Minimum redundancy feature selection from microarray gene expression data, J. Bioinform. Comput. Biol., 3, 185, 10.1142/S0219720005001004
Liu, 2005, An entropy-based gene selection method for cancer classification using microarray data, BMC Bioinformatics, 6, 76, 10.1186/1471-2105-6-76
Peng, 2005, Feature selection based on mutual information: criteria of max dependency, maxrelevance, and min-redundancy, IEEE Trans. Pattern Anal. Mach. Intel, 27, 1226, 10.1109/TPAMI.2005.159
Hu, 2006, A novel microarray gene selection method based on consistency
Mao, 2007, Correlation-based relevancy and redundancy measures for efficient gene selection, Lect. Notes Comput. Sci., 4774, 230, 10.1007/978-3-540-75286-8_23
Kim, 2006, A new maximum-relevance criterion for significant gene selection, Lect. Notes Comput. Sci., 4146, 71, 10.1007/11818564_9
Li, 2007, Optimal search-based gene subset selection for gene array cancer classification, IEEE Trans. INF. Technol. Biomed., 11, 398, 10.1109/TITB.2007.892693
Mundra, 2007, SVM-RFE with relevancy and redundancy criteria for gene selection, Lect. Notes Comput. Sci., 4774, 242, 10.1007/978-3-540-75286-8_24
Mamitsuka, 2006, Selecting features in microarray classification using ROC curves, Pattern Recognit., 39, 2393, 10.1016/j.patcog.2006.07.010
John, 1994, Irrelevant features and the subset selection problem, 121
Yu, 2004, Efficient feature selection via analysis of relevance and redundancy, J. Mach. Learn. Res., 5, 1205
Saeys, 2007, A review of feature selection techniques in bioinformatics, Bioinformatics, 23, 2507, 10.1093/bioinformatics/btm344
Dudoit, 2002, Comparison of discrimination methods for the classification of tumors using gene expression data, J. Am. Stat. Assoc., 97, 77, 10.1198/016214502753479248
Nguyen, 2002, Tumor classification by partial least squares using microarray gene expression data, Bioinformatics, 18, 39, 10.1093/bioinformatics/18.1.39
Ambroise, 2002, Selection bias in gene extraction on the basis of microarray geneexpression data, Proc. Natl. Acad. Sci. USA, 99, 6562, 10.1073/pnas.102102699
Troyanskaya, 2001, Missing value estimation methods for DNA microarrays, Bioinformatics, 17, 520, 10.1093/bioinformatics/17.6.520
Bicciato, 2001, Analysis of an associative memory neural network for pattern identification in gene expression data, 22
Cho, 2003, Machine learning in DNA microarray analysis for cancer classification, 189
Cho, 2007, Cancer classification using ensemble of neural networks with multiple significant gene subsets, Appl. Intell., 26, 243, 10.1007/s10489-006-0020-4
Chu, 2005, Biomarker discovery in microarray gene expression data with Gaussian processes, Bioinformatics, 21, 3385, 10.1093/bioinformatics/bti526
Tang, Y., et al. 2005. FCM-SVM-RFE gene feature selection algorithm for leukemia classification from microarray gene expression data. In Proceedings of the 14th IEEE International Conference on Fuzzy Systems, pp. 97-101. Reno, USA.
Wang, Z., et al. 2006. Neuro-fuzzy ensemble approach for microarray cancer gene expression data analysis. In Proceedings of the Second International Symposium on Evolving Fuzzy Systems, pp. 241-246. Lake District, UK.
Zhou, 2005, Gene Selection using Logistic Regressions based on Aic, Bic and MDL criteria, New Math. Nat. Comput, 1, 129, 10.1142/S179300570500007X
Tang, 2007, Development of two-stage SVM-RFE gene selection strategy for microarray expression data analysis, IEEE/ACM Trans. Comput. Biol. Bioinform, 4, 365, 10.1109/TCBB.2007.70224
Guyon, 2002, Gene selection for cancer classification using support vector machines, Mach. Learn., 46, 389, 10.1023/A:1012487302797
Park, 2001, A nonparametric scoring algorithm for identifying informative genes from microarray data, Pac. Symp. Biocomput., 52
Furey, 2000, Support vector machine classification and validation of cancer tissue samples using microarray expression data, Bioinformatics, 16, 906, 10.1093/bioinformatics/16.10.906
Li, 2001, Gene selection for sample classification based on gene expression data: study of sensitivity to choice of parameters of the GA/KNN method, Bioinformatics, 17, 1131, 10.1093/bioinformatics/17.12.1131
Li, 2002, How many genes are needed for a discriminant microarray data analysis?, 137
Marchiori, 2005, Bayesian learning with local support vector machines for cancer classification with gene expression data, Lect. Notes Comput. Sci., 3449, 74, 10.1007/978-3-540-32003-6_8
Yang, 2006, Generalized discriminant analysis for tumor classification with gene expression data, In Proceedings of the International Conference on Machine Learning and Cybernetics, 4322
Peng, 2006, A hybrid approach for biomarker discovery from microarray gene expression data for cancer classification, Cancer Informatics, 2, 301, 10.1177/117693510600200024
Li, 2007, Partial least squares based dimension reduction with gene selection for tumor classification, 1439
Zhang, 2007, An effective gene selection method based on relevance analysis and discernibility matrix, Lect. Notes Comput. Sci., 4426, 1088, 10.1007/978-3-540-71701-0_123
Schuchhardt, 2000, Normalization strategies for cDNA microarrays, Nucleic Acids Res., 28, E47, 10.1093/nar/28.10.e47
Ross, 2005
Tang, 2003, Interrelated clustering: an approach for gene expression data analysis, 183
Shannon, 1948, A mathematical theory of communication, Bell Syst. Tech. J, 27, 379, 10.1002/j.1538-7305.1948.tb01338.x
Steuer, 2002, The mutual information: detecting and evaluating dependencies between variables, Bioinformatics, 18, S231, 10.1093/bioinformatics/18.suppl_2.S231
Schlögl, 2002, Estimating the mutual information of an EEG-based Brain-Computer Interface, Biomed. Tech., 47, 3, 10.1515/bmte.2002.47.1-2.3