Gene selection for microarray data classification via subspace learning and manifold regularization
暂无分享,去创建一个
[1] Jason Weston,et al. Gene Selection for Cancer Classification using Support Vector Machines , 2002, Machine Learning.
[2] John Platt,et al. Probabilistic Outputs for Support vector Machines and Comparisons to Regularized Likelihood Methods , 1999 .
[3] Lei Zhang,et al. Tumor Classification Based on Non-Negative Matrix Factorization Using Gene Expression Data , 2011, IEEE Transactions on NanoBioscience.
[4] Qinghua Hu,et al. Non-convex regularized self-representation for unsupervised feature selection , 2015, Image Vis. Comput..
[5] S. Dudoit,et al. STATISTICAL METHODS FOR IDENTIFYING DIFFERENTIALLY EXPRESSED GENES IN REPLICATED cDNA MICROARRAY EXPERIMENTS , 2002 .
[6] C. A. Murthy,et al. Unsupervised Feature Selection Using Feature Similarity , 2002, IEEE Trans. Pattern Anal. Mach. Intell..
[7] Xiao Chen,et al. A multi-objective heuristic algorithm for gene expression microarray data classification , 2016, Expert Syst. Appl..
[8] N. Altman. An Introduction to Kernel and Nearest-Neighbor Nonparametric Regression , 1992 .
[9] S. Z. Zhang,et al. Efficient Sugarcane Transformation via bar Gene Selection , 2017, Tropical Plant Biology.
[10] Yangyang Li,et al. Self-representation based dual-graph regularized feature selection clustering , 2016, Neurocomputing.
[11] Guodong Zhao,et al. Feature Subset Selection for Cancer Classification Using Weight Local Modularity , 2016, Scientific Reports.
[12] Kim-Anh Lê Cao,et al. Multiclass classification and gene selection with a stochastic algorithm , 2009, Comput. Stat. Data Anal..
[13] Chih-Jen Lin,et al. LIBSVM: A library for support vector machines , 2011, TIST.
[14] Mehrnoosh Bazrafkan,et al. A novel sparse coding algorithm for classification of tumors based on gene expression data , 2016, Medical & Biological Engineering & Computing.
[15] Gene H. Golub,et al. Matrix computations (3rd ed.) , 1996 .
[16] Qingshan Jiang,et al. A centroid-based gene selection method for microarray data classification. , 2016, Journal of theoretical biology.
[17] Xiao Zheng,et al. Speckle noise reduction for optical coherence tomography images via non-local weighted group low-rank representation , 2017 .
[18] Lukasz A. Kurgan,et al. Knowledge discovery approach to automated cardiac SPECT diagnosis , 2001, Artif. Intell. Medicine.
[19] Xiao Wang,et al. Unsupervised feature selection via Diversity-induced Self-representation , 2017, Neurocomputing.
[20] M. R. Baring,et al. Advanced Backcross Quantitative Trait Loci (QTL) Analysis of Oil Concentration and Oil Quality Traits in Peanut (Arachis hypogaea L.) , 2017, Tropical Plant Biology.
[21] Byung Ro Moon,et al. Hybrid Genetic Algorithms for Feature Selection , 2004, IEEE Trans. Pattern Anal. Mach. Intell..
[22] Wei-Chung Cheng,et al. Gene selection for cancer identification: a decision tree model empowered by particle swarm optimization algorithm , 2014, BMC Bioinformatics.
[23] Mikhail Belkin,et al. Laplacian Eigenmaps and Spectral Techniques for Embedding and Clustering , 2001, NIPS.
[24] Jane Labadin,et al. Feature selection based on mutual information , 2015, 2015 9th International Conference on IT in Asia (CITA).
[25] David G. Stork,et al. Pattern classification, 2nd Edition , 2000 .
[26] Wei-Chung Cheng,et al. Microarray meta-analysis database (M2DB): a uniformly pre-processed, quality controlled, and manually curated human clinical microarray database , 2010, BMC Bioinformatics.
[27] K. Ma,et al. Feature selection and classification of urinary mRNA microarray data by iterative random forest to diagnose renal fibrosis: a two-stage study , 2017, Scientific Reports.
[28] Shichao Zhang,et al. Robust Joint Graph Sparse Coding for Unsupervised Spectral Feature Selection , 2017, IEEE Transactions on Neural Networks and Learning Systems.
[29] Dongmei Zhang,et al. Nonparametrically Guided Autoencoder with Laplace Approximation for dimensionality reduction , 2016, 2016 International Joint Conference on Neural Networks (IJCNN).
[30] Yudong D. He,et al. Gene expression profiling predicts clinical outcome of breast cancer , 2002, Nature.
[31] Alfonso González-Briones,et al. An Agent-Based Clustering Approach for Gene Selection in Gene Expression Microarray , 2017, Interdisciplinary Sciences: Computational Life Sciences.
[32] K. Morteza,et al. A novel sparse coding algorithm for classification of tumors based on gene expression data , 2017 .
[33] Qingshan Jiang,et al. A L1-regularized feature selection method for local dimension reduction on microarray data , 2017, Comput. Biol. Chem..
[34] J. Thomas,et al. An efficient and robust statistical modeling approach to discover differentially expressed genes using genomic expression profiles. , 2001, Genome research.
[35] Ron Kohavi,et al. A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection , 1995, IJCAI.
[36] Xiangtao Li,et al. Multiobjective ranking binary artificial bee colony for gene selection problems using microarray datasets , 2017 .
[37] Roberto Battiti,et al. Using mutual information for selecting features in supervised neural net learning , 1994, IEEE Trans. Neural Networks.
[38] Bart De Moor,et al. Predicting the prognosis of breast cancer by integrating clinical and microarray data with Bayesian networks , 2006, ISMB.
[39] Ivan P. Gavrilyuk,et al. Lagrange multiplier approach to variational problems and applications , 2010, Math. Comput..
[40] Debashis Ghosh,et al. Classification and Selection of Biomarkers in Genomic Data Using LASSO , 2005, Journal of biomedicine & biotechnology.
[41] Carla E. Brodley,et al. Feature Selection for Unsupervised Learning , 2004, J. Mach. Learn. Res..
[42] Chun-Hou Zheng,et al. Differentially expressed genes selection via Laplacian regularized low-rank representation method , 2016, Comput. Biol. Chem..
[43] Seymour Geisser,et al. 8. Predictive Inference: An Introduction , 1995 .
[44] Fuhui Long,et al. Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy , 2003, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[45] M. Gahr,et al. Distribution of estrogen receptors in the brain of the Japanese quail: an immunocytochemical study , 1989, Brain Research.
[46] Jiguo Yu,et al. An NMF-L2,1-Norm Constraint Method for Characteristic Gene Selection , 2016, PloS one.
[47] A D Long,et al. Improved Statistical Inference from DNA Microarray Data Using Analysis of Variance and A Bayesian Statistical Framework , 2001, The Journal of Biological Chemistry.
[48] Xiaowei Yang,et al. An efficient gene selection algorithm based on mutual information , 2009, Neurocomputing.
[49] Jinmao Wei,et al. Local-Nearest-Neighbors-Based Feature Weighting for Gene Selection , 2018, IEEE/ACM Transactions on Computational Biology and Bioinformatics.
[50] Xiaofei He,et al. Locality Preserving Projections , 2003, NIPS.
[51] Lei Wang,et al. Efficient Spectral Feature Selection with Minimum Redundancy , 2010, AAAI.
[52] Corinna Cortes,et al. Support-Vector Networks , 1995, Machine Learning.
[53] Igor V. Tetko,et al. Gene selection from microarray data for cancer classification - a machine learning approach , 2005, Comput. Biol. Chem..
[54] Chiara Sabatti,et al. Network component analysis: Reconstruction of regulatory signals in biological systems , 2003, Proceedings of the National Academy of Sciences of the United States of America.
[55] Li-Yeh Chuang,et al. A Hybrid BPSO-CGA Approach for Gene Selection and Classification of Microarray Data , 2012, J. Comput. Biol..
[56] Xin Zhou,et al. MSVM-RFE: extensions of SVM-RFE for multiclass gene selection on DNA microarray data , 2007, Bioinform..
[57] Tin Kam Ho,et al. The Random Subspace Method for Constructing Decision Forests , 1998, IEEE Trans. Pattern Anal. Mach. Intell..
[58] Yide Ma,et al. Robust unsupervised feature selection via matrix factorization , 2017, Neurocomputing.
[59] Xiaojun Wu,et al. Graph Regularized Nonnegative Matrix Factorization for Data Representation , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[60] Verónica Bolón-Canedo,et al. A review of microarray datasets and applied feature selection methods , 2014, Inf. Sci..
[61] J. Mesirov,et al. Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. , 1999, Science.
[62] Mohammad Hossein Moattar,et al. A hybrid gene selection approach for microarray data classification using cellular learning automata and ant colony optimization. , 2016, Genomics.
[63] Stephen P. Boyd,et al. Convex Optimization , 2004, Algorithms and Theory of Computation Handbook.
[64] Shiquan Sun,et al. A Kernel-Based Multivariate Feature Selection Method for Microarray Data Classification , 2014, PloS one.
[65] Deng Cai,et al. Laplacian Score for Feature Selection , 2005, NIPS.
[66] Pichao Wang,et al. Salient Object Detection via Weighted Low Rank Matrix Recovery , 2017, IEEE Signal Processing Letters.
[67] H. Sebastian Seung,et al. Learning the parts of objects by non-negative matrix factorization , 1999, Nature.
[68] Jing Liu,et al. Unsupervised Feature Selection Using Nonnegative Spectral Analysis , 2012, AAAI.
[69] Simon C. K. Shiu,et al. Unsupervised feature selection by regularized self-representation , 2015, Pattern Recognit..
[70] M. Hestenes. Multiplier and gradient methods , 1969 .
[71] Deng Cai,et al. Unsupervised feature selection for multi-cluster data , 2010, KDD.
[72] Huan Liu,et al. Spectral feature selection for supervised and unsupervised learning , 2007, ICML '07.
[73] Kazufumi Ito,et al. Lagrange multiplier approach to variational problems and applications , 2008, Advances in design and control.
[74] Ben Niu,et al. A discrete bacterial algorithm for feature selection in classification of microarray gene expression cancer data , 2017, Knowl. Based Syst..
[75] Josef Kittler,et al. Pattern recognition : a statistical approach , 1982 .
[76] Junbin Gao,et al. Gaussian Processes Autoencoder for Dimensionality Reduction , 2014, PAKDD.
[77] Feiping Nie,et al. Trace Ratio Criterion for Feature Selection , 2008, AAAI.