Ensemble gene selection by grouping for microarray data classification
Tài liệu tham khảo
Golub, 1999, Molecular classification of cancer: class discovery and class prediction by gene expression monitoring, Science, 286, 531, 10.1126/science.286.5439.531
Larrañaga, 2006, Machine learning in bioinformatics, Brief Bioinform, 7, 86, 10.1093/bib/bbk007
Dupuy, 2007, Critical review of published microarray studies for cancer outcome and guidelines on statistical analysis and reporting, J Natl Cancer Inst, 9, 147, 10.1093/jnci/djk018
Boulesteix, 2008, Evaluating microarray-based classifiers: an overview, Cancer Inform, 6, 77, 10.4137/CIN.S408
Natsoulis, 2005, Classification of a large microarray data set: algorithm comparison and analysis of drug signatures, Genome Res, 15, 724, 10.1101/gr.2807605
Somorjai, 2003, Class prediction and discovery using gene microarray and proteomics mass spectrometry data: curses, caveats, cautions, Bioinformatics, 19, 1484, 10.1093/bioinformatics/btg182
Saeys, 2007, A review of feature selection techniques in bioinformatics, Bioinformatics, 23, 2507, 10.1093/bioinformatics/btm344
Hilario, 2008, Approaches to dimensionality reduction in proteomic biomarker studies, Brief Bioinform, 9, 102, 10.1093/bib/bbn005
Nam, 2008, Gene-set approach for expression pattern analysis, Brief Bioinform, 9, 189, 10.1093/bib/bbn001
Ding, 2005, Minimum redundancy feature selection from microarray gene expression data, J Bioinform Comput Biol, 3, 185, 10.1142/S0219720005001004
Shen, 2009, New gene selection method for multiclass tumor classification by class centroid, J Biomed Inform, 42, 59, 10.1016/j.jbi.2008.05.011
Zhou, 2007, MSVM-RFE: extensions of SVM-RFE for multiclass gene selection on DNA microarray data, Bioinformatics, 23, 1106, 10.1093/bioinformatics/btm036
Yeh, 2008, Applying data mining techniques for cancer classification on gene expression data, Cybern Syst, 39, 583, 10.1080/01969720802188292
Zhu, 2007, Markov blanket-embedded genetic algorithm for gene selection, Pattern Recognit, 40, 3236, 10.1016/j.patcog.2007.02.007
Au, 2005, Attribute clustering for grouping, selection, and classification of gene expression data, IEEE/ACM Trans Comput Biol Bioinform, 2, 83, 10.1109/TCBB.2005.17
Yu L, Ding C, Loscalzo S. Stable feature selection via dense feature groups. In: Proceeding of the 14th ACM SIGKDD international conference on knowledge discovery and data mining. Las Vegas, USA: ACM; 2008. p. 803–11.
Boulesteix, 2008, Microarray-based classification and clinical predictors: on combined classifiers and additional predictive value, Bioinformatics, 24, 1698, 10.1093/bioinformatics/btn262
Díaz-Uriarte, 2006, Gene selection and classification of microarray data using random forest, BMC Bioinform, 7, 3, 10.1186/1471-2105-7-3
Moon, 2007, Ensemble methods for classification of patients for personalized medicine with high-dimensional data, Artif Intell Med, 41, 197, 10.1016/j.artmed.2007.07.003
Cho, 2003, Data mining for gene expression profiles from DNA microarray, Int J Software Eng Knowledge Eng, 13, 593, 10.1142/S0218194003001469
Cho, 2007, Cancer classification using ensemble of neural networks with multiple significant gene subsets, Appl Intell, 26, 243, 10.1007/s10489-006-0020-4
Saeys, 2007, Robust feature selection using ensemble feature selection techniques, 313
Wang, 2008, A general wrapper approach to selection of class-dependent features, IEEE Trans Neural Netw, 19, 1267, 10.1109/TNN.2008.2000395
Okun, 2008, Dataset complexity in gene expression based cancer classification using ensembles of k-nearest neighbors, Artif Intell Med
Yan, 2008, Selecting informative genes for discriminant analysis using multigene expression profiles, BMC Genomics, 9, S14, 10.1186/1471-2164-9-S2-S14
Ein-Dor, 2005, Outcome signature genes in breast cancer: is there a unique set?, Bioinformatics, 21, 171, 10.1093/bioinformatics/bth469
Zeng, 2008, Dimension reduction with redundant genes elimination for tumor classification, BMC Bioinform, 9, S8, 10.1186/1471-2105-9-S6-S8
Alexe, 2006, Pattern-based feature selection in genomics and proteomics, Ann Oper Res, 148, 189, 10.1007/s10479-006-0084-x
1991
Liu, 2009, Feature selection with dynamic mutual information, Pattern Recognit, 42, 1330, 10.1016/j.patcog.2008.10.028
Forman, 2003, An extensive empirical study of feature selection metrics for text classification, J Mach Learn Res, 3, 1289
Hua, 2009, Performance of feature-selection methods in the classification of high-dimension data, Pattern Recognit, 42, 409, 10.1016/j.patcog.2008.08.001
Li, 2004, A comparative study of feature selection and multiclass classification methods for tissue classification based on gene expression, Bioinformatics, 20, 2429, 10.1093/bioinformatics/bth267
Kerr, 2008, Techniques for clustering gene expression data, Comput Biol Med, 38, 283, 10.1016/j.compbiomed.2007.11.001
Yu, 2004, Efficient feature selection via analysis of relevance and redundancy, J Mach Learn Res, 5, 1205
Dietterich T. Ensemble methods in machine learning. In: Proceedings of the 1st international workshop on multiple classifier systems; 2000. p. 1–15.
Tsymbal, 2005, Diversity in search strategies for ensemble feature selection, Inf Fusion, 6, 83, 10.1016/j.inffus.2004.04.003
van’t Veer, 2002, Gene expression profiling predicts clinical outcome of breast cancer, Nature, 415, 530, 10.1038/415530a
Pomeroy, 2002, Prediction of central nervous system embryonal tumour outcome based on gene expression, Nature, 415, 436, 10.1038/415436a
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, 6745, 10.1073/pnas.96.12.6745
Singh, 2002, Gene expression correlates of clinical prostate cancer behavior, Cancer Cell, 1, 203, 10.1016/S1535-6108(02)00030-2
Langley P, Iba W, Thompson K. An analysis of Bayesian classifiers. In: Proceedings of the tenth national conference on artificial intelligence; 1992. p. 223–8.
Kira K, Rendell L. A practical approach to feature selection. In: Proceedings of the ninth international conference on machine learning. Morgan Kaufmann; 1992. p. 249–56.
Sima, 2005, Impact of error estimation on feature selection, Pattern Recognit, 38, 2472, 10.1016/j.patcog.2005.03.026
Yu, 2008, Feature selection for genomic data analysis, 337
Yang, 2006, A stable gene selection in microarray data analysis, BMC Bioinform, 7, 228, 10.1186/1471-2105-7-228
Davis, 2006, Reliable gene signatures for microarray classification: assessment of stability and performance, Bioinformatics, 22, 2356, 10.1093/bioinformatics/btl400
Domingos P. A unified bias-variance decomposition and its applications. In: Proceedings of the seventeenth international conference on machine learning. Morgan Kaufmann, San Fransisco; 2000. p. 231–38.