View Item 

Show simple item record

Bayesian classification of tumours by using gene expression data
dc.contributor.authorMallick, Bani K.en_US
dc.contributor.authorGhosh, Debashisen_US
dc.contributor.authorGhosh, Malayen_US
dc.date.accessioned2010-06-01T22:42:29Z
dc.date.available2010-06-01T22:42:29Z
dc.date.issued2005-04en_US
dc.identifier.citationMallick, Bani K.; Ghosh, Debashis; Ghosh, Malay (2005). "Bayesian classification of tumours by using gene expression data." Journal of the Royal Statistical Society: Series B (Statistical Methodology) 67(2): 219-234. <http://hdl.handle.net/2027.42/75678>en_US
dc.identifier.issn1369-7412en_US
dc.identifier.issn1467-9868en_US
dc.identifier.urihttps://hdl.handle.net/2027.42/75678
dc.format.extent149330 bytes
dc.format.extent3109 bytes
dc.format.mimetypeapplication/pdf
dc.format.mimetypetext/plain
dc.publisherBlackwell Publishing Ltden_US
dc.rights2005 Royal Statistical Societyen_US
dc.subject.otherGibbs Samplingen_US
dc.subject.otherMarkov Chain Monte Carlo Methodsen_US
dc.subject.otherMetropolis–Hastings Algorithmen_US
dc.subject.otherMicroarraysen_US
dc.subject.otherReproducing Kernel Hilbert Spaceen_US
dc.subject.otherShrinkage Parametersen_US
dc.subject.otherSupport Vector Machinesen_US
dc.titleBayesian classification of tumours by using gene expression dataen_US
dc.typeArticleen_US
dc.subject.hlbsecondlevelStatistics and Numeric Dataen_US
dc.subject.hlbtoplevelScienceen_US
dc.description.peerreviewedPeer Revieweden_US
dc.contributor.affiliationumUniversity of Michigan, Ann Arbor, USAen_US
dc.contributor.affiliationotherTexas A&M University, College Station, USAen_US
dc.contributor.affiliationotherUniversity of Florida, Gainesville, USAen_US
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/75678/1/j.1467-9868.2005.00498.x.pdf
dc.identifier.doi10.1111/j.1467-9868.2005.00498.xen_US
dc.identifier.sourceJournal of the Royal Statistical Society: Series B (Statistical Methodology)en_US
dc.identifier.citedreferenceAlbert, J. and Chib, S. ( 1993 ) Bayesian analysis of binary and polychotomous response data. J. Am. Statist. Ass., 88, 669 – 679.en_US
dc.identifier.citedreferenceAlon, U., Barkai, N., Notterman, D. A., Gish, K., Ybarra, S., Mack, D. and Levine, A. J. ( 1999 ) Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays. Proc. Natn. Acad. Sci. USA, 96, 6745 – 6750.en_US
dc.identifier.citedreferenceAronszajn, N. ( 1950 ) Theory of reproducing kernels. Trans. Am. Math. Soc., 68, 337 – 404.en_US
dc.identifier.citedreferenceBernardo, J. M. ( 1979 ) Expected information as expected utility. Ann. Statist., 7, 686 – 690.en_US
dc.identifier.citedreferenceBishop, C. and Tipping, M. ( 2000 ) Variational relevance vector machines. In Proc. 16th Conf. Uncertainty and Artificial Intelligence ( eds C. Boutilier and M. Goldszmidt ), pp. 46 – 53. San Francisco: Morgan Kauffman.en_US
dc.identifier.citedreferenceBrown, M. P. S., Grundy, W. N., Lin, D., Cristianini, N., Sugnet, C. W., Furey, T. S. and Haussler, D. ( 2000 ) Knowledge-based analysis of microarray gene expression data by using support vector machines. Proc. Natn. Acad. Sci. USA, 97, 262 – 267.en_US
dc.identifier.citedreferenceCristianini, N. and Shawe-Taylor, J. ( 2000 ) An Introduction to Support Vector Machines. Cambridge: Cambridge University Press.en_US
dc.identifier.citedreferenceDenison, D., Holmes, C., Mallick, B. and Smith, A. F. M. ( 2002 ) Bayesian Methods for Nonlinear Classification and Regression. London: Wiley.en_US
dc.identifier.citedreferenceDeRisi, J. L., Iyer, V. R. and Brown, P. O. ( 1997 ) Exploring the metabolic and genetic control of gene expression on a genomic scale. Science, 278, 680 – 685.en_US
dc.identifier.citedreferenceDudoit, S., Fridlyand, J. and Speed, T. P. ( 2002 ) Comparison of discrimination methods for the classification of tumors using gene expression data. J. Am. Statist. Ass., 97, 77 – 87.en_US
dc.identifier.citedreferenceFigueiredo, M. ( 2002 ) Adaptive sparseness using Jeffreys prior. In Neural Information Processing Systems 14 ( eds T. Dietterich, S. Becker and Z. Ghahramani ), pp. 697 – 704. Cambridge: MIT Press.en_US
dc.identifier.citedreferenceFix, E. and Hodges, J. L. ( 1951 ) Discriminatory analysis-nonparametric discrimination: consistency properties. US Air Force School of Aviation Medicine, Randolf Field.en_US
dc.identifier.citedreferenceGelfand, A. ( 1996 ) Model determination using sampling-based methods. In Markov Chain Monte Carlo in Practice ( eds W. R. Gilks, S. Richardson and D. J. Spiegelhalter ), pp. 145 – 158. London: Chapman and Hall.en_US
dc.identifier.citedreferenceGelfand, A. and Smith, A. F. M. ( 1990 ) Sampling-based approaches to calculating marginal densities. J. Am. Statist. Ass., 85, 398 – 409.en_US
dc.identifier.citedreferenceGelman, A. ( 1996 ) Inference and monitoring convergence. In Markov Chain Monte Carlo in Practice ( eds W. R. Gilks, S. Richardson and D. J. Spiegelhalter ), pp. 131 – 140. London: Chapman and Hall.en_US
dc.identifier.citedreferenceGolub, T. R., Slonim, D., Tamayo, P., Huard, C., Gaasenbeek, M., Mesirov, J., Coller, H., Loh, M., Downing, J., Caligiuri, M., Bloomfield, C. and Lander, E. ( 1999 ) Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science, 286, 531 – 537.en_US
dc.identifier.citedreferenceHastie, T. J. and Tibshirani, R. J. ( 1990 ) Generalized Additive Models. London: Chapman and Hall.en_US
dc.identifier.citedreferenceHedenfalk, I., Duggan, D., Chen, Y., Radmacher, M., Bittner, M., Simon, R., Meltzer, P., Gusterson, B., Esteller, M., Kallioniemi, O. P., Wilfond, B., Borg, A. and Trent, J. ( 2001 ) Gene expression profiles in hereditary breast cancer. New Engl. J. Med., 344, 539 – 548.en_US
dc.identifier.citedreferenceHerbrich, R. ( 2002 ) Learning Kernel Classifiers. Cambridge: MIT Press.en_US
dc.identifier.citedreferenceHolmes, C. and Held, L. ( 2003 ) Bayesian auxiliary variable models for binary and polychotomous regression. Technical Report. Imperial College, London.en_US
dc.identifier.citedreferenceKimeldorf, G. and Wahba, G. ( 1971 ) Some results on Tchebycheffian spline functions. J. Math. Anal. Applic., 33, 82 – 95.en_US
dc.identifier.citedreferenceLee, K. E., Sha, N., Dougherty, E., Vannucci, M. and Mallick, B. ( 2003 ) Gene selection: a Bayesian variable selection approach. Bioinformatics, 19, 90 – 97.en_US
dc.identifier.citedreferenceMacKay, D. ( 1996 ) Bayesian non-linear modelling for the 1993 energy prediction competition. In Maximum Entropy and Bayesian Methods ( ed. G. Heidbreder ), pp. 221 – 234. Dordrecht: Kluwer.en_US
dc.identifier.citedreferenceMetropolis, N., Rosenbluth, A., Rosenbluth, M., Teller, A. and Teller, E. ( 1953 ) Equation of state calculations by fast computing machines. J. Chem. Phys., 21, 1087 – 1092.en_US
dc.identifier.citedreferenceMoler, E. J., Chow, M. L. and Mian, I. S. ( 2000 ) Analysis of molecular profile data using generative and discriminative methods. Physiol. Genet., 4, 109 – 126.en_US
dc.identifier.citedreferenceNeal, R. ( 1996 ) Bayesian Learning for Neural Networks. New York: Springer.en_US
dc.identifier.citedreferenceParzen, E. ( 1970 ) Statistical inference on time series by rkhs methods. In Proc. 12th Bienn. Sem. ( ed. R. Pyke ), pp. 1 – 37. Montreal: Canadian Mathematical Congress.en_US
dc.identifier.citedreferencePontil, M., Evgeniou, T. and Poggio, T. ( 2000 ) Regularization networks and support vector machines. Adv. Comput. Math., 13, 1 – 50.en_US
dc.identifier.citedreferenceRipley, B. D. ( 1996 ) Pattern Recognition and Neural Networks. Cambridge: Cambridge University Press.en_US
dc.identifier.citedreferenceRosenblatt F. ( 1962 ) Principles of Neurodynamics. New York: Spartan.en_US
dc.identifier.citedreferenceRoth, V. ( 2002 ) The generalized LASSO: a wrapper approach to gene selection for microarray data. Department of Computer Science, University of Bonn, Bonn.en_US
dc.identifier.citedreferenceSchena, M., Shalon, D., Davis, R. and Brown, P. ( 1995 ) Quantitative monitoring of gene expression patterns with a complementary DNA microarray. Science, 270, 467 – 470.en_US
dc.identifier.citedreferenceSchÖlkopf, B. and Smola, A. ( 2002 ) Learning with Kernels. Cambridge: MIT Press.en_US
dc.identifier.citedreferenceSollich, P. ( 2001 ) Bayesian methods for support vector machines: evidence and predictive class probabilities. Mach. Learn., 46, 21 – 52.en_US
dc.identifier.citedreferenceSpecht, D. F. ( 1990 ) Probabilistic neural networks. Neur. Netwrks, 3, 109 – 118.en_US
dc.identifier.citedreferenceTipping, M. ( 2000 ) The relevance vector machine. In Neural Information Processing Systems 12 ( eds S. Solla, T. Leen and K. Muller ), pp. 652 – 658. Cambridge: MIT Press.en_US
dc.identifier.citedreferenceTipping, M. ( 2001 ) Sparse Bayesian learning and the relevance vector machine. J. Mach. Learn. Res., 1, 211 – 244.en_US
dc.identifier.citedreferenceVapnik, V. N. ( 2000 ) The Nature of Statistical Learning Theory, 2nd edn. New York: Springer.en_US
dc.identifier.citedreferenceWahba, G. ( 1990 ) Spline Models for Observational Data. Philadelphia: Society for Industrial and Applied Mathematics.en_US
dc.identifier.citedreferenceWahba, G. ( 1999 ) Support vector machines, reproducing kernel hilbert spaces and the randomized GACV. In Advances in Kernel Methods ( eds B. SchÖlkopf, C. Burges and A. Smola ), pp. 69 – 88. Cambridge: MIT Press.en_US
dc.identifier.citedreferenceWahba, G., Lin, Y., Lee, Y. and Zhang, H. ( 2002 ) Optimal properties and adaptive tuning of standard and nonstandard support vector machines. In Nonlinear Estimation and Classification ( eds D. Denison, M. Hansen, C. Holmes, B. Mallick and B. Yu ), pp. 125 – 143. New York: Springer.en_US
dc.identifier.citedreferenceWest, M. ( 2003 ) Bayesian factor regression models in the ‘‘large p, small n ’’ paradigm. In Bayesian Statistics 7 ( eds J. M. Bernardo, M. Bayarri, A. P. Dawid, J. Berger, D. Heckerman, A. F. M. Smith and M. West ), pp. 723 – 732. Oxford: Oxford University Press.en_US
dc.identifier.citedreferenceWilliams, C. and Barber, D. ( 1998 ) Bayesian classification with Gaussian priors. IEEE Trans. Pattn Anal. Mach. Intell., 20, 1342 – 1351.en_US
dc.identifier.citedreferenceXiong, M., Jin, L., Li, W. and Boerwinkle, E. ( 2000 ) Computational methods for gene expression-based tumor classification. Biotechniques, 29, 1264 – 1270.en_US
dc.identifier.citedreferenceZhu, J. and Hastie, T. ( 2002 ) Kernel logistic regression and the import vector machine. In Neural Information Processing Systems 14 ( eds T. Dietterich, S. Becker and Z. Ghahramani ), pp. 1081 – 1088. Cambridge: MIT Press.en_US
dc.owningcollnameInterdisciplinary and Peer-Reviewed


Files in this item

Name:
j.1467-9868.2005.00498.x.pdf
Size:
145.8KB
Format:
PDF
View/Open

Show simple item record

Remediation of Harmful Language

The University of Michigan Library aims to describe library materials in a way that respects the people and communities who create, use, and are represented in our collections. Report harmful or offensive language in catalog records, finding aids, or elsewhere in our collections anonymously through our metadata feedback form. More information at Remediation of Harmful Language.

Accessibility

If you are unable to use this file in its current format, please select the Contact Us link and we can modify it to make it more accessible to you.