Wrapper-based selection of genetic features in genome-wide association studies through fast matrix operations

[1]  Yan Wang,et al.  Genome-wide association study identifies two new susceptibility loci for atopic dermatitis in the Chinese Han population , 2011, Nature Genetics.

[2]  Philippa J. Talmud,et al.  Utility of genetic determinants of lipids and cardiovascular events in assessing risk , 2011, Nature Reviews Cardiology.

[3]  Nora Franceschini,et al.  Recent findings in the genetics of blood pressure and hypertension traits. , 2011, American journal of hypertension.

[4]  Satish Chikkagoudar,et al.  Ranking causal variants and associated regions in genome-wide association studies by the support vector machine and random forest , 2011, Nucleic acids research.

[5]  E. Boerwinkle,et al.  Mining gold dust under the genome wide significance level: a two‐stage approach to analysis of GWAS , 2011, Genetic epidemiology.

[6]  Tapio Salakoski,et al.  Speeding Up Greedy Forward Selection for Regularized Least-Squares , 2010, 2010 Ninth International Conference on Machine Learning and Applications.

[7]  Marcin Krawczyk,et al.  Genome-wide association studies and genetic risk assessment of liver diseases , 2010, Nature Reviews Gastroenterology &Hepatology.

[8]  Jon Genuneit,et al.  Unifying Candidate Gene and GWAS Approaches in Asthma , 2010, PloS one.

[9]  Tero Aittokallio,et al.  Genetic Variants and Their Interactions in the Prediction of Increased Pre-Clinical Carotid Atherosclerosis: The Cardiovascular Risk in Young Finns Study , 2010, PLoS genetics.

[10]  Wenjiang J. Fu,et al.  Mapping Haplotype-haplotype Interactions with Adaptive LASSO , 2010, BMC Genetics.

[11]  A. Butte,et al.  Extreme Evolutionary Disparities Seen in Positive Selection across Seven Complex Diseases , 2010, PloS one.

[12]  Nancy R Cook,et al.  Association between a literature-based genetic risk score and cardiovascular events in women. , 2010, JAMA.

[13]  Jason H. Moore,et al.  BIOINFORMATICS REVIEW , 2005 .

[14]  Kui Zhang,et al.  Genome-wide association studies of rheumatoid arthritis data via multiple hypothesis testing methods for correlated tests , 2009 .

[15]  Soonil Kwon,et al.  Application of Bayesian classification with singular value decomposition method in genome-wide association studies , 2009, BMC proceedings.

[16]  Andrei S. Rodin,et al.  Use of Wrapper Algorithms Coupled with a Random Forests Classifier for Variable Selection in Large-Scale Genomic Association Studies , 2009, J. Comput. Biol..

[17]  Ingrid B. Borecki,et al.  Multiple Genes Influence BMI on Chromosome 7q31–34: The NHLBI Family Heart Study , 2009, Obesity.

[18]  R. Plomin,et al.  Common disorders are quantitative traits , 2009, Nature Reviews Genetics.

[19]  Ian H. Witten,et al.  The WEKA data mining software: an update , 2009, SKDD.

[20]  Joseph T. Glessner,et al.  From Disease Association to Risk Assessment: An Optimistic View from Genome-Wide Association Studies on Type 1 Diabetes , 2009, PLoS genetics.

[21]  Peter M Visscher,et al.  Harnessing the information contained within genome-wide association studies to improve individual prediction of complex disease risk. , 2009, Human molecular genetics.

[22]  Scott M. Williams,et al.  Epistasis and its implications for personal genetics. , 2009, American journal of human genetics.

[23]  L. Zimmerli,et al.  Angiogenesis and hypertension: an update , 2009, Journal of Human Hypertension.

[24]  Andrew D. Johnson,et al.  Genome-wide association study of blood pressure and hypertension , 2009, Nature Genetics.

[25]  Nicholette D. Palmer,et al.  A genome-wide association scan for acute insulin response to glucose in Hispanic-Americans: the Insulin Resistance Atherosclerosis Family Study (IRAS FS) , 2009, Diabetologia.

[26]  J. Suykens,et al.  A kernel-based integration of genome-wide data for clinical decision support , 2009, Genome Medicine.

[27]  Tapio Pahikkala,et al.  An efficient algorithm for learning to rank from preference graphs , 2009, Machine Learning.

[28]  Scott T. Weiss,et al.  A Genome-Wide Association Study of Pulmonary Function Measures in the Framingham Heart Study , 2009, PLoS genetics.

[29]  Chuhsing Kate Hsiao,et al.  A new regularized least squares support vector regression for gene selection , 2009, BMC Bioinformatics.

[30]  Daniel E. Weeks,et al.  Interpretation of Genetic Association Studies: Markers with Replicated Highly Significant Odds Ratios May Be Poor Classifiers , 2009, PLoS genetics.

[31]  Jason H. Moore,et al.  Exploiting the proteome to improve the genome-wide genetic analysis of epistasis in common human diseases , 2008, Human Genetics.

[32]  Qingzhong Liu,et al.  Supervised learning-based tagSNP selection for genome-wide disease classifications , 2008, BMC Genomics.

[33]  Johan A. K. Suykens,et al.  Low rank updated LS-SVM classifiers for fast variable selection , 2008, Neural Networks.

[34]  Burton B. Yang,et al.  MicroRNA-378 promotes cell survival, tumor growth, and angiogenesis by targeting SuFu and Fus-1 expression , 2007, Proceedings of the National Academy of Sciences.

[35]  K. Weigel,et al.  Machine learning classification procedure for selecting SNPs in genomic selection: application to early mortality in broilers. , 2007, Developments in biologicals.

[36]  Manuel A. R. Ferreira,et al.  PLINK: a tool set for whole-genome association and population-based linkage analyses. , 2007, American journal of human genetics.

[37]  Simon C. Potter,et al.  Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls , 2007, Nature.

[38]  R. Rifkin,et al.  Notes on Regularized Least Squares , 2007 .

[39]  Jin-Kao Hao,et al.  A Hybrid GA/SVM Approach for Gene Selection and Classification of Microarray Data , 2006, EvoWorkshops.

[40]  V. Vapnik.  Estimation of Dependences Based on Empirical Data , 2006 .

[41]  Xin Yao,et al.  Gene selection algorithms for microarray data based on least squares support vector machine , 2006, BMC Bioinformatics.

[42]  Richard Simon,et al.  Bias in error estimation when using cross-validation for model selection , 2006, BMC Bioinformatics.

[43]  T. Poggio,et al.  The Mathematics of Learning: Dealing with Data , 2005, 2005 International Conference on Neural Networks and Brain.

[44]  Hilda Silva Ferreira,et al.  Role of central 5-HT3 receptors in the control of blood pressure in stressed and non-stressed rats , 2004, Brain Research.

[45]  Mika Kähönen,et al.  Geographic Origin as a Determinant of Carotid Artery Intima-Media Thickness and Brachial Artery Flow-Mediated Dilation: The Cardiovascular Risk in Young Finns Study , 2004, Arteriosclerosis, thrombosis, and vascular biology.

[46]  Miriam C J M Sturkenboom,et al.  Genetic polymorphisms and heart failure , 2004, Genetics in Medicine.

[47]  Jing Peng,et al.  SVM vs regularized least squares classification , 2004, Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004..

[48]  Maria Petrou,et al.  Proceedings of the 17th International Conference on Pattern Recognition (ICPR 2004) , 2004 .

[49]  Pedro Larrañaga,et al.  Filter versus wrapper gene selection approaches in DNA microarray domains , 2004, Artif. Intell. Medicine.

[50]  Edward R. Dougherty,et al.  Is cross-validation better than resubstitution for ranking genes? , 2004, Bioinform..

[51]  Wei Du,et al.  Molecular classification of cancer types from microarray data using the combination of genetic algorithms and support vector machines , 2003, FEBS letters.

[52]  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.

[53]  Glenn Fung,et al.  Proximal support vector machine classifiers , 2001, KDD '01.

[54]  Nello Cristianini,et al.  Support vector machine classification and validation of cancer tissue samples using microarray expression data , 2000, Bioinform..

[55]  Nir Friedman,et al.  Tissue classification with gene expression profiles , 2000, RECOMB '00.

[56]  Tomaso A. Poggio,et al.  Regularization Networks and Support Vector Machines , 2000, Adv. Comput. Math..

[57]  Arthur E. Hoerl,et al.  Ridge Regression: Biased Estimation for Nonorthogonal Problems , 2000, Technometrics.

[58]  Johan A. K. Suykens,et al.  Least Squares Support Vector Machine Classifiers , 1999, Neural Processing Letters.

[59]  Alexander Gammerman,et al.  Ridge Regression Learning Algorithm in Dual Variables , 1998, ICML.

[60]  Ron Kohavi,et al.  Wrappers for Feature Subset Selection , 1997, Artif. Intell..

[61]  Ron Kohavi,et al.  Irrelevant Features and the Subset Selection Problem , 1994, ICML.

[62]  F. Girosi,et al.  Networks for approximation and learning , 1990, Proc. IEEE.

[63]  Vladimir Vapnik,et al.  Estimation of Dependences Based on Empirical Data: Springer Series in Statistics (Springer Series in Statistics) , 1982 .

[64]  A. E. Hoerl,et al.  Ridge Regression: Applications to Nonorthogonal Problems , 1970 .

[65]  P. Lachenbruch.  An almost unbiased method of obtaining confidence intervals for the probability of misclassification in discriminant analysis. , 1967, Biometrics.

[66]  Qianchuan He,et al.  A variable selection method for genome-wide association studies , 2011, Bioinform..

[67]  Robert H. Halstead,et al.  Matrix Computations , 2011, Encyclopedia of Parallel Computing.

[68]  David M. Reif,et al.  Machine Learning for Detecting Gene-Gene Interactions: A Review , 2011 .

[69]  Jin-Kao Hao,et al.  Advances in metaheuristics for gene selection and classification of microarray data , 2010, Briefings Bioinform..

[70]  G. D'Angelo,et al.  Combining least absolute shrinkage and selection operator (LASSO) and principal-components analysis for detection of gene-gene interactions in genome-wide association studies , 2009, BMC proceedings.

[71]  Yan V. Sun,et al.  Machine learning in genome‐wide association studies , 2009, Genetic epidemiology.

[72]  Pedro Larrañaga,et al.  A review of feature selection techniques in bioinformatics , 2007, Bioinform..

[73]  Tapio Salakoski,et al.  Fast n-Fold Cross-Validation for Regularized Least-Squares , 2006 .

[74]  Tapani Raiko,et al.  Proceedings of the Ninth Scandinavian Conference on Artificial Intelligence (SCAI 2006) , 2006 .

[75]  V Kren,et al.  A 14-gene region of rat chromosome 8 in SHR-derived polydactylous congenic substrain affects muscle-specific insulin resistance, dyslipidaemia and visceral adiposity. , 2005, Folia biologica.

[76]  Nello Cristianini,et al.  Kernel Methods for Pattern Analysis , 2004 .

[77]  M. Pontil.  Leave-one-out error and stability of learning algorithms with applications , 2002 .

[78]  Tomaso Poggio,et al.  Everything old is new again: a fresh look at historical approaches in machine learning , 2002 .

[79]  Elizabeth M. Smigielski,et al.  dbSNP: the NCBI database of genetic variation , 2001, Nucleic Acids Res..

[80]  David Meyer,et al.  Support Vector Machines ∗ The Interface to libsvm in package e1071 , 2001 .

[81]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[82]  S. R. Searle,et al.  On Deriving the Inverse of a Sum of Matrices , 1981 .

[83]  Park,et al.  Open Access Research Article Identification of Type 2 Diabetes-associated Combination of Snps Using Support Vector Machine , 2022 .

[84]  R. Shah,et al.  Least Squares Support Vector Machines , 2022 .