A review of feature selection techniques in bioinformatics
暂无分享,去创建一个
Pedro Larrañaga | Yvan Saeys | Iñaki Inza | Y. Saeys | Iñaki Inza | P. Larrañaga | Yvan Saeys | Pedro Larrañaga | Pedro Larrañaga
[1] David G. Stork,et al. Pattern Classification , 1973 .
[2] John H. Holland,et al. Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .
[3] Chi Hau Chen,et al. Pattern recognition and signal processing , 1978 .
[4] Jack Perkins,et al. Pattern recognition in practice , 1980 .
[5] Laveen N. Kanal,et al. Classification, Pattern Recognition and Reduction of Dimensionality , 1982, Handbook of Statistics.
[6] Jack Sklansky,et al. On Automatic Feature Selection , 1988, Int. J. Pattern Recognit. Artif. Intell..
[7] David B. Skalak,et al. Prototype and Feature Selection by Sampling and Random Mutation Hill Climbing Algorithms , 1994, ICML.
[8] Henrik I. Christensen,et al. Pattern Recognition in Practice IV: Multiple Paradigms, Comparative Studies and Hybrid Systems , 1994 .
[9] Daphne Koller,et al. Toward Optimal Feature Selection , 1996, ICML.
[10] Ron Kohavi,et al. Data Mining Using MLC a Machine Learning Library in C++ , 1996, Int. J. Artif. Intell. Tools.
[11] S. Salzberg,et al. Microbial gene identification using interpolated Markov models. , 1998, Nucleic acids research.
[12] Ron Kohavi,et al. Feature Selection for Knowledge Discovery and Data Mining , 1998 .
[13] Antonia J. Jones,et al. Feature selection for genetic sequence classification , 1998, Bioinform..
[14] J. Mesirov,et al. Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. , 1999, Science.
[15] S. Salzberg,et al. Improved microbial gene identification with GLIMMER. , 1999, Nucleic acids research.
[16] U. Alon,et al. Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays. , 1999, Proceedings of the National Academy of Sciences of the United States of America.
[17] Ian H. Witten,et al. Data mining: practical machine learning tools and techniques, 3rd Edition , 1999 .
[18] Nir Friedman,et al. Tissue classification with gene expression profiles , 2000, RECOMB '00.
[19] Ian Witten,et al. Data Mining , 2000 .
[20] Pedro Larrañaga,et al. Feature Subset Selection by Bayesian network-based optimization , 2000, Artif. Intell..
[21] Christian A. Rees,et al. Systematic variation in gene expression patterns in human cancer cell lines , 2000, Nature Genetics.
[22] Pierre Baldi,et al. A Bayesian framework for the analysis of microarray expression data: regularized t -test and statistical inferences of gene changes , 2001, Bioinform..
[23] 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.
[24] J. Thomas,et al. An efficient and robust statistical modeling approach to discover differentially expressed genes using genomic expression profiles. , 2001, Genome research.
[25] Michael I. Jordan,et al. Feature selection for high-dimensional genomic microarray data , 2001, ICML.
[26] Thomas A. Darden,et al. Gene selection for sample classification based on gene expression data: study of sensitivity to choice of parameters of the GA/KNN method , 2001, Bioinform..
[27] Shigeo Abe DrEng. Pattern Classification , 2001, Springer London.
[28] M. Xiong,et al. Biomarker Identification by Feature Wrappers , 2022 .
[29] Christina Kendziorski,et al. On Differential Variability of Expression Ratios: Improving Statistical Inference about Gene Expression Changes from Microarray Data , 2001, J. Comput. Biol..
[30] Wentian Li,et al. How Many Genes are Needed for a Discriminant Microarray Data Analysis , 2001, physics/0104029.
[31] M. Daly,et al. High-resolution haplotype structure in the human genome , 2001, Nature Genetics.
[32] John D. Storey,et al. Empirical Bayes Analysis of a Microarray Experiment , 2001 .
[33] D. Nickerson,et al. Variation is the spice of life , 2001, Nature Genetics.
[34] T. H. Bø,et al. New feature subset selection procedures for classification of expression profiles , 2002, Genome Biology.
[35] Peter J. Park,et al. A Nonparametric Scoring Algorithm for Identifying Informative Genes from Microarray Data , 2000, Pacific Symposium on Biocomputing.
[36] E. Petricoin,et al. Use of proteomic patterns in serum to identify ovarian cancer , 2002, The Lancet.
[37] G. Li,et al. An integrated approach utilizing artificial neural networks and SELDI mass spectrometry for the classification of human tumours and rapid identification of potential biomarkers , 2002, Bioinform..
[38] Bernard De Baets,et al. Feature subset selection for splice site prediction , 2002, ECCB.
[39] Michael B. Eisen,et al. Identification of regulatory elements using a feature selection method , 2002, Bioinform..
[40] S. Gabriel,et al. The Structure of Haplotype Blocks in the Human Genome , 2002, Science.
[41] Jaques Reifman,et al. Support vector machines with selective kernel scaling for protein classification and identification of key amino acid positions , 2002, Bioinform..
[42] John D. Storey. A direct approach to false discovery rates , 2002 .
[43] Huiqing Liu,et al. A comparative study on feature selection and classification methods using gene expression profiles and proteomic patterns. , 2002, Genome informatics. International Conference on Genome Informatics.
[44] S. Dudoit,et al. Comparison of Discrimination Methods for the Classification of Tumors Using Gene Expression Data , 2002 .
[45] Geoffrey J McLachlan,et al. Selection bias in gene extraction on the basis of microarray gene-expression data , 2002, Proceedings of the National Academy of Sciences of the United States of America.
[46] J. Downing,et al. Classification, subtype discovery, and prediction of outcome in pediatric acute lymphoblastic leukemia by gene expression profiling. , 2002, Cancer cell.
[47] Russ B. Altman,et al. Nonparametric methods for identifying differentially expressed genes in microarray data , 2002, Bioinform..
[48] Saurabh Sinha,et al. Discriminative motifs , 2002, RECOMB '02.
[49] Patrick Tan,et al. Genetic algorithms applied to multi-class prediction for the analysis of gene expression data , 2003, Bioinform..
[50] Richard Baumgartner,et al. Class prediction and discovery using gene microarray and proteomics mass spectroscopy data: curses, caveats, cautions , 2003, Bioinform..
[51] Emanuel F Petricoin,et al. Mass spectrometry-based diagnostics: the upcoming revolution in disease detection. , 2003, Clinical chemistry.
[52] David Ward,et al. Comparison of statistical methods for classification of ovarian cancer using mass spectrometry data , 2003, Bioinform..
[53] Walter Daelemans,et al. Combined Optimization of Feature Selection and Algorithm Parameter Interaction in Machine Learning of Language , 2003 .
[54] Mark A. Hall,et al. Correlation-based Feature Selection for Machine Learning , 2003 .
[55] Bernhard Schölkopf,et al. Use of the Zero-Norm with Linear Models and Kernel Methods , 2003, J. Mach. Learn. Res..
[56] Chris H. Q. Ding,et al. Minimum redundancy feature selection from microarray gene expression data , 2003, Computational Systems Bioinformatics. CSB2003. Proceedings of the 2003 IEEE Bioinformatics Conference. CSB2003.
[57] Marina Vannucci,et al. Gene selection: a Bayesian variable selection approach , 2003, Bioinform..
[58] George Forman,et al. An Extensive Empirical Study of Feature Selection Metrics for Text Classification , 2003, J. Mach. Learn. Res..
[59] S. Dudoit,et al. Multiple Hypothesis Testing in Microarray Experiments , 2003 .
[60] Sergio Verjovski-Almeida,et al. ESTWeb: bioinformatics services for EST sequencing projects , 2003, Bioinform..
[61] Yvan Saeys,et al. Feature selection for splice site prediction: A new method using EDA-based feature ranking , 2004, BMC Bioinformatics.
[62] Roger E Bumgarner,et al. Multiclass classification of microarray data with repeated measurements: application to cancer , 2003, Genome Biology.
[63] Wei Pan,et al. On the Use of Permutation in and the Performance of A Class of Nonparametric Methods to Detect Differential Gene Expression , 2003, Bioinform..
[64] Isabelle Guyon,et al. An Introduction to Variable and Feature Selection , 2003, J. Mach. Learn. Res..
[65] Vladimir Pavlovic,et al. RankGene: identification of diagnostic genes based on expression data , 2003, Bioinform..
[66] Anne-Lise Veuthey,et al. Combining NLP and probabilistic categorisation for document and term selection for Swiss-Prot medical annotation , 2003, ISMB.
[67] Rainer Breitling,et al. Rank products: a simple, yet powerful, new method to detect differentially regulated genes in replicated microarray experiments , 2004, FEBS letters.
[68] Tao Li,et al. A comparative study of feature selection and multiclass classification methods for tissue classification based on gene expression , 2004, Bioinform..
[69] Ilya Levner,et al. Feature selection and nearest centroid classification for protein mass spectrometry , 2005, BMC Bioinformatics.
[70] Robert Tibshirani,et al. Sample classification from protein mass spectrometry, by 'peak probability contrasts' , 2004, Bioinform..
[71] Michael J. Becich,et al. Tests for finding complex patterns of differential expression in cancers: towards individualized medicine , 2004, BMC Bioinformatics.
[72] Paul Terry,et al. Application of the GA/KNN method to SELDI proteomics data , 2004, Bioinform..
[73] Pedro Larrañaga,et al. Filter versus wrapper gene selection approaches in DNA microarray domains , 2004, Artif. Intell. Medicine.
[74] Andrew Kusiak,et al. Data mining and genetic algorithm based gene/SNP selection , 2004, Artif. Intell. Medicine.
[75] R. Altman,et al. Finding haplotype tagging SNPs by use of principal components analysis. , 2004, American journal of human genetics.
[76] Elena Marchiori,et al. Feature selection in proteomic pattern data with support vector machines , 2004, 2004 Symposium on Computational Intelligence in Bioinformatics and Computational Biology.
[77] Byoung-Tak Zhang,et al. PubMiner: Machine Learning-based Text Mining for Biomedical Information Analysis , 2004 .
[78] Soohyun Lee,et al. CHOISS for selection of single nucleotide polymorphism markers on interval regularity , 2004, Bioinform..
[79] ROSA BLANCO,et al. Gene Selection For Cancer Classification Using Wrapper Approaches , 2004, Int. J. Pattern Recognit. Artif. Intell..
[80] J. Stuart Aitken,et al. Feature selection and classification for microarray data analysis: Evolutionary methods for identifying predictive genes , 2005, BMC Bioinformatics.
[81] Pietro Liò,et al. Identification of DNA regulatory motifs using Bayesian variable selection , 2004, Bioinform..
[82] C. Carlson,et al. Selecting a maximally informative set of single-nucleotide polymorphisms for association analyses using linkage disequilibrium. , 2004, American journal of human genetics.
[83] Huiqing Liu,et al. Using amino acid patterns to accurately predict translation initiation sites , 2004, Silico Biol..
[84] Huan Liu,et al. Efficient Feature Selection via Analysis of Relevance and Redundancy , 2004, J. Mach. Learn. Res..
[85] Jun Chen,et al. Joint analysis of two microarray gene-expression data sets to select lung adenocarcinoma marker genes , 2004, BMC Bioinformatics.
[86] Gordon K Smyth,et al. Linear Models and Empirical Bayes Methods for Assessing Differential Expression in Microarray Experiments , 2004, Statistical applications in genetics and molecular biology.
[87] Melanie Hilario,et al. Mining mass spectra for diagnosis and biomarker discovery of cerebral accidents , 2004, Proteomics.
[88] Edward R. Dougherty,et al. Is cross-validation valid for small-sample microarray classification? , 2004, Bioinform..
[89] Cheng Cheng,et al. Improving false discovery rate estimation , 2004, Bioinform..
[90] Adrian E. Raftery,et al. Normal uniform mixture differential gene expression detection for cDNA microarrays , 2005, BMC Bioinformatics.
[91] Huan Liu,et al. Toward integrating feature selection algorithms for classification and clustering , 2005, IEEE Transactions on Knowledge and Data Engineering.
[92] Edward R. Dougherty,et al. Superior feature-set ranking for small samples using bolstered error estimation , 2005, Bioinform..
[93] Byoung-Tak Zhang,et al. miTarget: microRNA target gene prediction using a support vector machine , 2006, BMC Bioinformatics.
[94] Adrian E. Raftery,et al. Bayesian model averaging: development of an improved multi-class, gene selection and classification tool for microarray data , 2005, Bioinform..
[95] Xia Li,et al. Application of a Genetic Algorithm - Support Vector Machine Hybrid for Prediction of Clinical Phenotypes Based on Genome-Wide SNP Profiles of Sib Pairs , 2005, FSKD.
[96] Jiangsheng Yu,et al. Bayesian neural network approaches to ovarian cancer identification from high-resolution mass spectrometry data , 2005, ISMB.
[97] Jian Huang,et al. Regularized ROC method for disease classification and biomarker selection with microarray data , 2005, Bioinform..
[98] Claudio Cobelli,et al. Ovarian cancer identification based on dimensionality reduction for high-throughput mass spectrometry data , 2005, Bioinform..
[99] Eran Halperin,et al. Tag SNP selection in genotype data for maximizing SNP prediction accuracy , 2005, ISMB.
[100] Jean Yee Hwa Yang,et al. Gene expression Identifying differentially expressed genes from microarray experiments via statistic synthesis , 2005 .
[101] Annette M. Molinaro,et al. Prediction error estimation: a comparison of resampling methods , 2005, Bioinform..
[102] Ali Al-Shahib,et al. Feature Selection and the Class Imbalance Problem in Predicting Protein Function from Sequence , 2005, Applied bioinformatics.
[103] William R. Hersh,et al. A survey of current work in biomedical text mining , 2005, Briefings Bioinform..
[104] Ramón Díaz-Uriarte,et al. Gene selection and classification of microarray data using random forest , 2006, BMC Bioinformatics.
[105] Habtom W. Ressom,et al. Analysis of mass spectral serum profiles for biomarker selection , 2005, Bioinform..
[106] Wei Zhang,et al. Large-Scale Ensemble Decision Analysis of Sib-Pair IBD Profiles for Identification of the Relevant Molecular Signatures for Alcoholism , 2005, FSKD.
[107] Constantin F. Aliferis,et al. A comprehensive evaluation of multicategory classification methods for microarray gene expression cancer diagnosis , 2004, Bioinform..
[108] Pierre Geurts,et al. Proteomic mass spectra classification using decision tree based ensemble methods , 2005, Bioinform..
[109] Debashis Ghosh,et al. Classification and Selection of Biomarkers in Genomic Data Using LASSO , 2005, Journal of biomedicine & biotechnology.
[110] Richard J. Fox,et al. A two-sample Bayesian t-test for microarray data , 2006, BMC Bioinformatics.
[111] Jae Won Lee,et al. An extensive comparison of recent classification tools applied to microarray data , 2004, Comput. Stat. Data Anal..
[112] Igor V. Tetko,et al. Gene selection from microarray data for cancer classification - a machine learning approach , 2005, Comput. Biol. Chem..
[113] Rainer Spang,et al. twilight; a Bioconductor package for estimating the local false discovery rate , 2005, Bioinform..
[114] P. Conilione,et al. A Comparative Study on Feature Selection for E . coli Promoter Recognition A Comparative Study on Feature Selection for E . coli Promoter Recognition , 2006 .
[115] Jesús S. Aguilar-Ruiz,et al. Incremental wrapper-based gene selection from microarray data for cancer classification , 2006, Pattern Recognit..
[116] P. Bork,et al. Literature mining for the biologist: from information retrieval to biological discovery , 2006, Nature Reviews Genetics.
[117] Slobodan Vucetic. Substring selection for biomedical document classification , 2006, TMBIO '06.
[118] M. Hilario,et al. Processing and classification of protein mass spectra. , 2006, Mass spectrometry reviews.
[119] Jeffrey T. Leek,et al. Gene expression EDGE : extraction and analysis of differential gene expression , 2006 .
[120] Hagit Shatkay,et al. BNTagger: improved tagging SNP selection using Bayesian networks , 2006, ISMB.
[121] Alex Zelikovsky,et al. MLR-tagging: informative SNP selection for unphased genotypes based on multiple linear regression , 2006, Bioinform..
[122] Yuhang Wang,et al. Tumor classification based on DNA copy number aberrations determined using SNP arrays. , 2006, Oncology reports.
[123] Bart De Moor,et al. Predicting the prognosis of breast cancer by integrating clinical and microarray data with Bayesian networks , 2006, ISMB.
[124] Pavlos Pavlidis,et al. Individualized markers optimize class prediction of microarray data , 2006, BMC Bioinformatics.
[125] Ljubomir J. Buturovic,et al. PCP: a program for supervised classification of gene expression profiles , 2006, Bioinform..
[126] Jill P. Mesirov,et al. Comparative gene marker selection suite , 2006, Bioinform..
[127] Gabriela Alexe,et al. A robust meta‐classification strategy for cancer detection from MS data , 2006, Proteomics.
[128] Xuegong Zhang,et al. Recursive SVM feature selection and sample classification for mass-spectrometry and microarray data , 2006, BMC Bioinformatics.
[129] Francesco Falciani,et al. GALGO: an R package for multivariate variable selection using genetic algorithms , 2006, Bioinform..
[130] Francisco Azuaje,et al. An assessment of recently published gene expression data analyses: reporting experimental design and statistical factors , 2006, BMC Medical Informatics Decis. Mak..
[131] Hiroshi Mamitsuka,et al. Selecting features in microarray classification using ROC curves , 2006, Pattern Recognit..
[132] Edward R. Dougherty,et al. What should be expected from feature selection in small-sample settings , 2006, Bioinform..
[133] Mia K. Markey,et al. A machine learning perspective on the development of clinical decision support systems utilizing mass spectra of blood samples , 2006, J. Biomed. Informatics.
[134] Michal Linial,et al. Novel Unsupervised Feature Filtering of Biological Data , 2006, ISMB.
[135] Yudi Pawitan,et al. Multidimensional local false discovery rate for microarray studies , 2006, Bioinform..
[136] Yvan Saeys,et al. In search of the small ones: improved prediction of short exons in vertebrates, plants, fungi and protists , 2007, Bioinform..
[137] Habtom W. Ressom,et al. Peak selection from MALDI-TOF mass spectra using ant colony optimization , 2007, Bioinform..
[138] Joaquín Dopazo,et al. Prophet, a web-based tool for class prediction using microarray data , 2007, Bioinform..
[139] Jeffrey S. Morris,et al. Pre-Processing Mass Spectrometry Data , 2007 .
[140] Sio Iong Ao,et al. Combining functional and linkage disequilibrium information in the selection of tag SNPs , 2007, Bioinform..