References
2014; Wiley; Linguagem: Inglês
10.1002/9781118364505.refs
ISSN1940-6347
AutoresDhammika Amaratunga, Javier Cabrera, Ziv Shkedy,
Tópico(s)Bioinformatics and Genomic Networks
ResumoFree Access References Dhammika Amaratunga, Dhammika Amaratunga Janssen Pharmaceutical Companies of Johnson and Johnson, USASearch for more papers by this authorJavier Cabrera, Javier Cabrera Rutgers University, USASearch for more papers by this authorZiv Shkedy, Ziv Shkedy Hasselt University, BelgiumSearch for more papers by this author Book Author(s):Dhammika Amaratunga, Dhammika Amaratunga Janssen Pharmaceutical Companies of Johnson and Johnson, USASearch for more papers by this authorJavier Cabrera, Javier Cabrera Rutgers University, USASearch for more papers by this authorZiv Shkedy, Ziv Shkedy Hasselt University, BelgiumSearch for more papers by this author First published: 24 February 2014 https://doi.org/10.1002/9781118364505.refsBook Series:Wiley Series in Probability and Statistics AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat References Agresti A. Categorical Data Analysis. 2nd ed. New York: John Wiley & Sons; 2002. Wiley Online LibraryGoogle Scholar Alberts B, Bray D, Lewis J, Raff M, Roberts K, Watson J. Molecular Biology of the Cell. New York: Addison-Wesley; 1994. Google Scholar Aldenderfer MS, Blashfield RK. Cluster Analysis. London: Sage Publications; 1984. CrossrefWeb of Science®Google Scholar Alizadeh AA, Eisen MB, Davis RE, Ma C, Lossos IS, Rosenwald A, Boldrick JC, Sabet H, Tran T, Yu X, Powell JI, Yang L, Marti GE, Moore T, Hudson J Jr., Lu L, Lewis DB, Tibshirani R, Sherock G, Chan WC, Greiner TC, Weisenburger DD, Armitage JO, Warnke R, Levy R, Wilson W, Grever MR, Byrd JC, Botstein D, Brown PO, Staudt LM. Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling. Nature 2000; 403: 503– 511. CrossrefCASPubMedWeb of Science®Google Scholar Allison DB, Gadbury GL, Heo M, Fernndez JR, Lee CK, Prolla TA, Weindruch R. A mixture model approach for the analysis of microarray gene expression data. Comput Stat Data Anal 2002; 39: 1– 20. CrossrefWeb of Science®Google Scholar Alon U, Barkai N, Notterman DA, Gish K, Ybarra S, Mack D, Levine AJ. Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays. Proc Natl Acad Sci U S A 1999; 96: 6745– 6750. CrossrefCASPubMedWeb of Science®Google Scholar Alter O, Brown PO, Botstein D. Singular value decomposition for genome-wide expression data processing and modeling. Proc Natl Acad Sci U S A 2000; 97: 10101d2– 1010106. CrossrefWeb of Science®Google Scholar Amaratunga D, Cabrera J. Statistical analysis of microchip data. Unpublished material presented at the Joint Statistical Meetings. Baltimore, MD; 1999. Google Scholar Amaratunga D, Cabrera J. Outlier resistance, standardization and modeling issues for DNA microarray data. In: Fernholz LT, Morgenthaler S, Stahel W, editors. Statistics and Genetics for the Environmental Sciences. Basel, Switzerland: Birkhauser Verlag; 2001a. Google Scholar Amaratunga D, Cabrera J. Statistical analysis of viral microchip data. J Am Stat Assoc 2001b; 96: 1161– 1170. CrossrefWeb of Science®Google Scholar Amaratunga D, Cabrera J. Mining data to find subsets of high activity. J Stat Plann Infer 2003a; 122: 23– 41. CrossrefWeb of Science®Google Scholar Amaratunga D, Cabrera J. Methods for assessing the quality of DNA microarrays; 2003b, Unpublished. Google Scholar Amaratunga D, Cabrera J. A robust Bayes analysis of DNA microarray data; 2003c, Unpublished. Google Scholar Amaratunga D, Cabrera J. A conditional t suite of tests for identifying differentially expressed genes in a DNA microarray experiment with little replication. Stat Biopharm Res 2009; 1: 26– 38. CrossrefWeb of Science®Google Scholar Amaratunga D, Cabrera J, Kovtun V. Microarray learning with ABC. Biostatistics 2008; 9: 128– 136. CrossrefPubMedWeb of Science®Google Scholar Amaratunga D, Cabrera J, Cherkas Y, Lee YS. Ensemble classifiers. In: Fourdrinier D, Marchand E, Rukhin AL, editors. Beachwood, Ohio, USA: IMS Collection Volume 8: Contemporary Developments in Bayesian Analysis and Statistical Decision Theory: A Festschrift for William E. Strawderman; 2012. Google Scholar Amaratunga D, Cabrera J, De Bondt A, Tryputsen V. Using Fisher's method to identify enriched gene sets. Unpublished; 2013. Google Scholar Amaratunga D, Cabrera J, Lee YS. Enriched random forests. Bioinformatics 2008; 24: 2010– 2014. CrossrefCASPubMedWeb of Science®Google Scholar Ambroise C, McLachlan GJ. Selection bias in gene extraction on basis of microarray gene expression data. Proc Natl Acad Sci U S A 2002; 99: 6562– 6566. CrossrefCASPubMedWeb of Science®Google Scholar Angenendt P, Glokler J, Murphy D, Lehrach H, Cahill DJ. Toward optimized antibody microarrays: a comparison of current microarray support materials. Anal Biochem 2002; 309: 253– 260. CrossrefCASPubMedWeb of Science®Google Scholar Anscombe F, Tukey JW. The examination and analysis of residuals. Technometrics 1963; 5: 141– 160. CrossrefWeb of Science®Google Scholar Asimov D. The grand tour: a tool for viewing multidimensional data. SIAM J Sci Stat Comput 1985; 6: 128– 143. CrossrefWeb of Science®Google Scholar Astrand M. Contrast normalization of oligonucleotide arrays. J Comput Biol 2003; 10: 95– 102. CrossrefCASPubMedWeb of Science®Google Scholar Baldi P, Long AD. A Bayesian framework for the analysis of microarray expression data: regularized t-test and statistical inferences of gene changes. Bioinformatics 2001; 7: 509– 519. CrossrefWeb of Science®Google Scholar Banfield JD, Raftery AE. Model-based Gaussian and non-Gaussian clustering. Biometrics 1993; 49: 803– 821. CrossrefWeb of Science®Google Scholar Barash Y, Friedman N. Context-specific Bayesian clustering for gene expression data. J Comput Biol 2002; 9: 169– 191. CrossrefCASPubMedWeb of Science®Google Scholar Barnett V, editor. Interpreting Multivariate Data. New York: John Wiley & Sons; 1981. Google Scholar Barnett V, Lewis T. Outliers in Statistical Data. 3rd ed. New York: John Wiley & Sons; 1994. Web of Science®Google Scholar Barlow RE, Bartholomew DJ, Bremner MJ, Brunk HD. Statistical Inference Under Order Restriction. New York: John Wiley & Sons; 1972. Google Scholar Bassett DE, Eisen MB, Boguski MS. Gene expression informatics - it's all in your mine. Nat Genet Suppl 1999; 21: 51– 55. CrossrefCASPubMedWeb of Science®Google Scholar Ben-Hur A, Elisseeff A, Guyon I. A stability-based method for discovering structure in clustered data. Pac Symp Biocomput 2002; 7: 6– 17. Google Scholar Benjamini Y, Hochberg Y. Controlling the False Discovery Rate: a practical and powerful approach to multiple testing. J R Stat Soc B 1995; 57: 289– 300. Wiley Online LibraryGoogle Scholar Benjamini Y, Yekutieli D. The control of the false discovery rate in multiple testing under dependency. Ann Stat 2001; 29(4): 1165– 1188. CrossrefWeb of Science®Google Scholar Bittner M, Meltzer P, Chen Y, Jiang Y, Seftor E, Hendrix M, Radmacher M, Simon R, Yakhini Z, Ben-Dor A, Sampas N, Dougherty E, Wang E, Marincola F, Gooden C, Lueders J, Glatfelter A, Pollock P, Carpten J, Gillanders E, Leja D, Dietrich K, Beaudry C, Berens M, Alberts D, Sondak V, Hayward N, Trent J. Molecular classification of cutaneous malignant melanoma by gene expression profiling. Nature 2000; 406: 536– 540. CrossrefCASPubMedWeb of Science®Google Scholar Blower PE, Yang C, Fligner MA, Verducci JS, Yu L, Richman S, Weinstein JN. Pharmacogenomic analysis: correlating molecular substructure classes with microarray gene expression data. Pharmacogenomics J 2002; 2: 259– 271. CrossrefCASPubMedGoogle Scholar Bo TH, Jonassen I. New feature subset selection procedures for classification of expression profiles. Genome Biol 2002; 3:(research) 0017-1-0017-11. CrossrefCASPubMedWeb of Science®Google Scholar Bolstad BM, Irizarry RA, Astrand M, Speed TP. A comparison of normalization methods for high density oligonucleotide array data based on variance and bias. Bioinformatics 2003; 19: 185– 193. CrossrefCASPubMedWeb of Science®Google Scholar Bourgon R, Gentleman R, Huber W. Independent filtering increases detection power for high-throughput experiments. Proc Natl Acad Sci U S A 2010; 107(21): 9546– 9551. CrossrefCASPubMedWeb of Science®Google Scholar Bouton CM, Pevsner J. DRAGON: database referencing of array genes online. Bioinformatics 2000; 16: 1038– 1039. CrossrefCASPubMedWeb of Science®Google Scholar Bouton C, Pevsner J. DRAGON view: information visualization for annotated microarray data. Bioinformatics 2002; 18: 323– 324. CrossrefCASPubMedWeb of Science®Google Scholar Brazma A, Vilo J. Gene expression data analysis. FEBS Lett 2000; 480: 17– 24. Wiley Online LibraryCASPubMedWeb of Science®Google Scholar Breiman L. Bagging predictors. Mach Learn 1996; 26: 123– 140. CrossrefWeb of Science®Google Scholar Breiman L, Cutler A. Random Forests Manual (version 4.0), Technical Report of the University of California. Berkeley: Department of Statistics; 2003. Google Scholar Breiman L. Random forests. Mach Learn 2001; 45: 5– 32. CrossrefWeb of Science®Google Scholar Breiman L, Friedman JH, Olshen RA, Stone CJ. Classification and Regression Trees. Monterey (CA): Wadsworth; 1984. Google Scholar Brillinger DR, Fernholz LT, Morgenthaler S, editors. The Practice of Data Analysis. Princeton (NJ): Princeton University Press; 1997. CrossrefGoogle Scholar Broberg P. Ranking genes with respect to differential expression. Genome Biol 2002; 3, preprint 0007.1-preprint 0007.23. CrossrefWeb of Science®Google Scholar Brown CS, Goodwin PC, Sorger PK. Image metrics in the statistical analysis of DNA microarray data. Proc Natl Acad Sci U S A 2001; 98: 8944– 8949. CrossrefCASPubMedWeb of Science®Google Scholar Brown MP, Grundy WN, Lin D, Cristianini N, Sugnet CW, Furey TS, Ares M Jr., Haussler D. Knowledge-based analysis of microarray gene expression data by using support vector machines. Proc Natl Acad Sci U S A 2000; 97: 262– 267. CrossrefCASPubMedWeb of Science®Google Scholar Brown PO, Botstein D. Exploring the new world of the genome with DNA microarrays. Nat Genet Suppl 1999; 21: 33– 37. CrossrefCASPubMedWeb of Science®Google Scholar Buja A, Lee Y. S. Data mining criteria for tree-based regression and classification, KDD 2001: Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2001; 27– 36. Google Scholar Burges CJC. A tutorial on support vector machines for pattern recognition. Data Mining Knowl Discov 1998; 2: 121– 167. CrossrefWeb of Science®Google Scholar Cabrera J, Fernholz LT. Target estimation for bias and mean square reduction. Ann Stat 1999; 27: 1080– 1104. Web of Science®Google Scholar Cabrera J, McDougall A. Statistical Consulting. New York: Springer-Verlag; 2001. Google Scholar Calinski T, Harabasz J. A dendrite method for cluster analysis. Commun Stat 1974; 3: 1– 27. CrossrefGoogle Scholar Calza S, Raffelsberger W, Ploner A, Sahel J, Leveillard T, Pawitan Y. Filtering genes to improve sensitivity in oligonucleotide microarray data analysis. Nucleic Acids Res 2007; 35(16). CrossrefCASWeb of Science®Google Scholar Chambers J, Angulo A, Amaratunga D, Guo H, Jiang Y, Wan JS, Bittner A, Frueh K, Jackson MR, Peterson PA, Erlander MG, Ghazal P. DNA microarrays of the complex human cytomegalovirus genome: profiling kinetic class with drug sensitive viral gene expression. J Virol 1999; 73: 5757– 5766. CASPubMedWeb of Science®Google Scholar Chambers JM, Cleveland WS, Kleiner B, Tukey PA. Graphical Methods for Data Analysis. Boston (MA): Duxbury Press; 1983. Google Scholar Chapman S, Schenk P, Kazan K, Manners J. Using biplots to interpret gene expression patterns in plants. Bioinformatics 2002; 18: 202– 204. CrossrefCASPubMedWeb of Science®Google Scholar Chen Y, Dougherty ED, Bittner ML. Ratio-based decisions and the quantitative analysis of cDNA microarray images. J Biomed Opt 1997; 2: 364– 374. CrossrefCASPubMedGoogle Scholar Cherkas Y. Classification and multiple testing for microarray data [PhD dissertation]. Newark (NJ): Rutgers University; 2010. Google Scholar Chu S, DeRisi J, Eisen M, Mulholland J, Botstein D, Brown PO, Herskowitz I. The transcriptional program of sporulation in budding yeast. Science 1998; 282: 699– 705. CrossrefCASPubMedWeb of Science®Google Scholar Chu T-M, Weir B, Wolfinger R. A systematic statistical linear modeling approach to oligonucleotide array experiments. Math Biosci 2002; 176: 35– 51. CrossrefCASPubMedWeb of Science®Google Scholar Chu T-M, Weir B, Wolfinger R. Comparison of Li-Wong and loglinear mixed models for the statistical analysis of oligonucleotide arrays. Bioinformatics 2004; 20: 500– 506. CrossrefCASPubMedWeb of Science®Google Scholar Churchill GA. Fundamentals of experimental design for cDNA microarrays. Nat Genet 2002; 32: 490– 495. CrossrefCASPubMedWeb of Science®Google Scholar Churchill GA, Oliver B. Sex, flies, and microarrays. Nat Genet 2001; 29: 355– 356. CrossrefCASPubMedWeb of Science®Google Scholar Clark D, Russell L. Molecular Biology Made Simple and Fun. Vienna (IL): Cache River Press; 1997. Google Scholar Clark LA, Pregibon D. Tree-based models. In: Chambers J, Hastie TJ, editors. Statistical Models in S. Wadsworth; 1992. Google Scholar Clark PJ, Evans FC. Distance to nearest neighbor as a measure of spatial relationships in populations. Ecology 1954; 35: 445– 453. Wiley Online LibraryWeb of Science®Google Scholar Cleveland WS. Robust locally weighted regression and smoothing scatterplots. J Am Stat Assoc 1979; 74: 829– 836. CrossrefWeb of Science®Google Scholar Cochran WG, Cox GM. Experimental Designs. New York: John Wiley & Sons; 1992. Google Scholar Cochran WG. Sampling Techniques. New York: John Wiley & Sons; 1977. Google Scholar Colantuoni C, Zeger S, Pevsner J. Local mean normalization of microarray element signal intensities across an array surface: quality control and correction of spatially systematic hybridization artifacts. Biotechniques 2002; 32: 1316– 1320. CASPubMedWeb of Science®Google Scholar Cook D, Buja A, Cabrera J. Projection pursuit indices based on orthogonal function expansions. J Comput Graph Stat 1993; 2: 225– 250. CrossrefGoogle Scholar Cook D, Buja A, Cabrera J, Hurley C. Grand tour and projection pursuit. J Comput Graph Stat 1995; 4: 155– 172. CrossrefGoogle Scholar Coombes KR. PCANOVA: Combining principal components with analysis of variance to assess group structure; 2002, Unpublished. Google Scholar Cormack RM. A review of classification. J R Stat Soc [Ser A] 1971; 134: 321– 367. Wiley Online LibraryWeb of Science®Google Scholar Cox DR, Hinkley D. Theoretical Statistics. London: Chapman and Hall; 1974. CrossrefGoogle Scholar Cui X, Kerr MK, Churchill GA. Data transformation for cDNA microarray data; 2002, Unpublished. Google Scholar Daniel C, Wood FS. Fitting Equations to Data. New York: John Wiley & Sons; 1971. Google Scholar Debouck C, Goodfellow PN. DNA microarrays in drug discovery and development. Nat Genet Suppl 1999; 21: 48– 50. CrossrefCASPubMedWeb of Science®Google Scholar Dempster AP, Laird NM, Rubin DB. Maximum likelihood from incomplete data via the EM algorithm. J R Stat Soc Ser B 1977; 39: 1– 38. Wiley Online LibraryGoogle Scholar DeRisi JL, Iyer VR, Brown PO. Exploring the metabolic and genetic control of gene expression on a genomic scale. Science 1997; 278: 680– 686. CrossrefCASPubMedWeb of Science®Google Scholar Dudoit S, Fridlyand J. A prediction-based resampling method to estimate the number of clusters in a dataset. Genome Biol 2002; 3: 0036-1-0036-21. CrossrefCASGoogle Scholar Dudoit S, Fridlyand J, Speed T. Comparison of discrimination methods for the classification of tumors using gene expression data. J Am Stat Assoc 2002; 97: 77– 87. CrossrefCASWeb of Science®Google Scholar Dudoit S, Yang YH, Callow MC, Speed TP. Statistical methods for identifying differentially expressed genes in replicated cDNA microarray experiments. Stat Sin 2002; 12: 111– 140. Web of Science®Google Scholar Durbin B, Hardin J, Hawkins D, Rocke DM. A variance-stabilizing transformation for gene expression microarray data. Bioinformatics 2002; 18: S105– S110. CrossrefPubMedWeb of Science®Google Scholar Efron B, Hastie T, Johnstone I, Tibshirani R. Least angle regression. Ann Stat 2004; 32: 407– 451. CrossrefWeb of Science®Google Scholar Efron B. Robbins, empirical Bayes, and microarrays. Technical Report of the Stanford University Department of Statistics; 2001. Google Scholar Efron B, Tibshirani R. An Introduction to the Bootstrap. London: Chapman and Hall; 1993. CrossrefGoogle Scholar Efron B, Tibshirani R. Empirical Bayes methods and false discovery rates for microarrays. Genet Epidemiol 2002; 23: 70– 86. Wiley Online LibraryCASPubMedWeb of Science®Google Scholar Efron B, Tibshirani R. On testing the significance of sets of genes. Ann Appl Stat 2007; 1: 107– 129. CrossrefWeb of Science®Google Scholar Efron B, Tibshirani R, Storey JD, Tusher V. Empirical Bayes analysis of a microarray experiment. J Am Stat Assoc 2001; 96: 1151– 1160. CrossrefWeb of Science®Google Scholar Efron B, Storey JD, Tibshirani R. Microarrays, empirical Bayes methods, and false discovery rates. Technical Report of the Stanford University Department of Statistics; 2001. Google Scholar Eisen MB, Spellman PT, Brown PO, Botstein D. Cluster analysis and display of genome-wide expression patterns. Proc Natl Acad Sci U S A 1998; 95: 14863– 14868. CrossrefCASPubMedWeb of Science®Google Scholar Everitt BS. Cluster Analysis. 3rd ed. London: Halsted Press; 1993. CrossrefWeb of Science®Google Scholar Ewens WJ, Grant GR. Statistical Methods in Bioinformatics: An Introduction. Secaucus (NJ): Springer-Verlag; 2001. CrossrefGoogle Scholar Friedman J, Hastie T, Tibshirani R. Regularization paths for generalized linear models via coordinate descent. J Stat Softw 2010; 33: 1– 22. CrossrefPubMedWeb of Science®Google Scholar Fayyad UM, Piatetsky-Shapiro G, Smyth P, editors. Advances in Knowledge Discovery and Data Mining. Menlo Park (CA): AAAI Press / MIT Press; 1996. Google Scholar Fellenberg K, Hauser N, Brors B, Neutzner A, Hoheisel J, Vingron M. Correspondence analysis applied to microarray data. Proc Natl Acad Sci U S A 2001; 98: 10781– 10786. CrossrefCASPubMedWeb of Science®Google Scholar Fernholz LT, Morgenthaler S, Stahel W, editors. Statistics in Genetics and in the Environmental Sciences. Basel, Switzerland: Birkhauser-Verlag; 2001. CrossrefGoogle Scholar Fisher RA. The use of multiple measurements in taxonomic problems. Ann Eugen 1936; 7: 179– 188. Wiley Online LibraryPubMedWeb of Science®Google Scholar Fisher RA. On the interpretation of χ2 from contingency tables and the calculation of p. J R Stat Soc Ser B 1922; 85: 87– 94. Wiley Online LibraryGoogle Scholar Fisher RA. Statistical Methods for Research Workers. Oliver and Boyd; 1925. Web of Science®Google Scholar Fisher RA. The Design of Experiments. 6th ed. London: Oliver and Boyd; 1951. Google Scholar Fix E, Hodges J. Discriminatory analysis. nonparametric discrimination: consistency properties. Technical Report of the USAF School of Aviation Medicine, Randolph Field (TX); 1951. Google Scholar Fraley C, Raftery AE. How many clusters? Which clustering method? Answers via model-based cluster analysis. Comput J 1998; 41: 578– 588. CrossrefWeb of Science®Google Scholar Freund Y, Schapire RE. A decision-theoretic generalization of on-line learning and an application to boosting. J Comput Syst Sci 1997; 55: 119– 139. CrossrefWeb of Science®Google Scholar Friedman JH. Exploratory projection pursuit. J Am Stat Assoc 1987; 82: 249– 266. CrossrefWeb of Science®Google Scholar Friedman JH. Regularized discriminant analysis. J Am Stat Assoc 1989; 84: 165– 175. CrossrefWeb of Science®Google Scholar Friedman JH. Flexible metric nearest neighbor classification. Technical Report of the Stanford University Statistics Department; 1994. Google Scholar Friedman JH, Meulman JJ. Clustering objects on subsets of attributes. J R Stat Soc Ser B 2004; 66: 815– 849. Wiley Online LibraryWeb of Science®Google Scholar Friedman JH, Stuetzle W. Projection pursuit regression. J Am Stat Assoc 1981; 76: 817– 823. CrossrefPubMedWeb of Science®Google Scholar Friedman JH, Tukey JW. A projection pursuit algorithm for exploratory data analysis. IEEE Trans Comput 1974; C-23: 881– 890. CrossrefWeb of Science®Google Scholar Gabriel KR. The biplot graphical display of matrices with applications to principal component analysis. Biometrika 1971; 58: 453– 467. CrossrefWeb of Science®Google Scholar Gabriel KR, Odoroff CL. Biplots in biomedical research. Stat Med 1990; 9: 469– 485. Wiley Online LibraryCASPubMedWeb of Science®Google Scholar Getz G, Levine E, Domany E. Coupled two-way clustering analysis of gene microarray data. Proc Natl Acad Sci U S A 2000; 97: 12079– 12084. CrossrefCASPubMedWeb of Science®Google Scholar Gentleman R, Carey V, Huber W, Irizarry R, Dudoit S. Bioinformatics and Computational Biology Solutions using R and Bioconductor. Springer; 2005. CrossrefGoogle Scholar Gibson G. Microarrays in ecology and evolution: a preview. Ecology 2002; 11: 17– 24. Wiley Online LibraryGoogle Scholar Glasbey CA, Ghazal P. Combinatorial image analysis of DNA microarray features. Bioinformatics 2003; 19: 194– 203. CrossrefCASPubMedWeb of Science®Google Scholar Gnanadesikan R. Statistical Data Analysis of Multivariate Observations. 2nd ed. New York: John Wiley & Sons; 1997. Wiley Online LibraryGoogle Scholar Gnanadesikan R, Kettenring JR. Discriminant analysis and clustering. Stat Sci 1989; 4: 34– 69. CrossrefGoogle Scholar Gohlmann H, Talloen W. Gene Expression Studies Using Affymetrix Microarrays. Taylor & Francis; 2009. Google Scholar Golub TR, Slonim DK, Tamayo P, Huard C, Gaasenbeek M, Mesirov JP, Coller H, Loh ML, Downing JR, Caligiuri MA, Bloomfield CD, Lander ES. Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 1999; 286: 531– 537. CrossrefCASPubMedWeb of Science®Google Scholar Gonick L, Wheelis M. A Cartoon Guide to Genetics. New York: Harper Collins; 1991. Google Scholar Gordon AD. Classification. Boca Raton (FL): Chapman and Hall/CRC; 1999. CrossrefWeb of Science®Google Scholar Haab B, Dunham M, Brown P. Protein microarrays for highly parallel detection and quantitation of specific proteins and antibodies in complex solutions. Genome Biol 2001; 2: Research 00004.1– Research 00004.13. CrossrefGoogle Scholar Hall P. Polynomial projection pursuit. Ann Stat 1989; 17: 589– 605. CrossrefWeb of Science®Google Scholar Hand DJ. Construction and Assessment of Classification Rules. New York: John Wiley & Sons; 1997. Google Scholar Hartigan JA. Direct clustering of a data matrix. J Am Stat Assoc 1972; 67: 123– 129. CrossrefWeb of Science®Google Scholar Hartigan JA. Clustering Algorithms. New York: John Wiley & Sons; 1975. Google Scholar Hastie T, Tibshirani R, Buja A. Flexible discriminant analysis. J Am Stat Assoc 1994; 89: 1255– 1270. Web of Science®Google Scholar Hastie T, Tibshirani R, Eisen MB, Alizadeh A, Levy R, Staudt L, Chan WC, Botstein D, Brown P. "Gene shaving" as a method for identifying distinct sets of genes with similar expression patterns. Genome Biol 2000; 1: Research 0003.1-0003.21. CrossrefCASPubMedWeb of Science®Google Scholar Hastie T, Tibshirani R, Friedman J. Elements of Statistical Learning: Data Mining, Inference and Prediction, Second Edition, Heidelberg: Springer-Verlag; 2009. Google Scholar Hawkins DM, Kass GV. Automatic interaction detection. In: Hawkins DM, editors. Topics in Multivariate Analysis. Cambridge: Cambridge University Press; 1982. Google Scholar Hearst M. SVM - trends and controversies. IEEE Intell Syst 1998; 13: 18. CrossrefWeb of Science®Google Scholar Hedenfalk I, Duggan D, Chen Y, Radmacher M, Bittner M, Simon R, Meltzer P, Gusterson B, Esteller M, Kallioniemi OP, Wilfond B, Borg A, Trent J. Gene-expression profiles in hereditary breast cancer. N Engl J Med 2001; 344: 539– 548. CrossrefCASPubMedWeb of Science®Google Scholar Hoaglin DC. Exploratory data analysis. In: Kotz S, Johnson NL, Read CB, editors. Encyclopedia of Statistical Sciences. Volume 2. New York: John Wiley & Sons; 1982. p 579– 583. Google Scholar Hoaglin DC, Mosteller F, Tukey JW. Understanding Robust and Exploratory Data Analysis. New York: John Wiley & Sons; 1983. Web of Science®Google Scholar Hochberg Y. A sharper Bonferroni procedure for multiple tests of significance. Biometrika 1988; 75: 800– 803. CrossrefPubMedWeb of Science®Google Scholar Hochreiter S, Bodenhofer U, Heusel M, Mayr A, Mitterecker A, Kasim A, Khamiakova T, Van Sanden S, Lin D, Talloen W, Bijnens L, Göhlmann HWH, Shkedy Z, Clevert D-A. FABIA: factor analysis for bicluster acquisition. Bioinformatics 2010; 26(12): 1520– 1527. CrossrefCASPubMedWeb of Science®Google Scholar Hoffmann R, Seidl T, Dugas M. Profound effect of normalization on detection of differentially expressed genes in oligonucleotide microarray data analysis. Genome Biol 2002; 3:research 0033-1-0033-11. Google Scholar Hollander M, Wolfe DA. Nonparametric Statistical Methods. 2nd ed. New York: John Wiley & Sons; 1999. Google Scholar Holloway AJ, Van Laar RK, Tothill RW, Bowtell DD. Options available - from start to finish - for obtaining data from DNA microarrays II. Nat Genet 2002; 32: 481– 489. CrossrefCASPubMedWeb of Science®Google Scholar Holm S. A simple sequentially rejective multiple test procedure. Scand J Stat 1979; 6: 65– 70. Web of Science®Google Scholar Holmes I, Bruno WJ. Finding regulatory elements using joint likelihoods for sequence and expression profile data. In: Altman R, et al., editors. Proceedings of the Eighth Annual International Conference on Intelligent Systems for Molecular Biology. La Jolla (CA): AAAI Press; 2000. p 202– 210. Google Scholar Hosack DA, Dennis G Jr, Sherman BT, Lane HC, Lempicki RA. Identifying biological themes within lists of genes with EASE. Genome Biol 2003; 4: R70. CrossrefPubMedWeb of Science®Google Scholar Huber W, Heydebreck AV, Silvermaan H, Poustka A, Vingron M. Variance stabilization applied to microarray data calibration and to the quantification of differential expression. Bioinformatics 2002; 18: 1– 9. Web of Science®Google Scholar Iglewicz B and Hoaglin D. Volume 16: How to Detect and Handle Outliers, The ASQC Basic References in Quality Control: Statistical Techniques, Edward F. Mykytka, Editor, 1993. Google Scholar Ihaka R, Gentleman R. R: a language for data analysis and graphics. J Comput Graph Stat 1996; 5: 299– 314. CrossrefGoogle Scholar Irizarry RA, Bolstad BM, Collin F, Cope LM, Hobbs B, Speed TP. Summaries of Affymetrix GeneChip probe level data. Nucleic Acids Res 2003; 31(4): 1– 8. CrossrefCASPubMedWeb of Science®Googl
Referência(s)