Bayesian variable selection for disease classification using gene expression data

Bioinformatics

Volumn 26, Issue 2, 2010, Pages 215-222

  Ai jun, Yang ^a   Xin yuan, Song ^a  

^a CHINESE UNIVERSITY OF HONG KONG   (Hong Kong)

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; BAYES THEOREM; CLASSIFICATION; COLON TUMOR; DNA MICROARRAY; GENE EXPRESSION PROFILING; GENERAL ASPECTS OF DISEASE; GENETIC DATABASE; GENETICS; HUMAN; LEUKEMIA; METHODOLOGY;

BAYES THEOREM; COLONIC NEOPLASMS; DATABASES, GENETIC; DISEASE; GENE EXPRESSION PROFILING; HUMANS; LEUKEMIA; OLIGONUCLEOTIDE ARRAY SEQUENCE ANALYSIS;

EID: 77950497193     PISSN: 13674803     EISSN: 14602059     Source Type: Journal    
DOI: 10.1093/bioinformatics/btp638     Document Type: Article

References (51)

1. Albert, J. and Chib, S. (1993) Bayesian analysis of binary and polychotomous response data. J. Am. Stat. Assoc., 88, 669-679.
2. Alon, U. et al. (1999) Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays. Proc. Natl Acad. Sci. USA, 96, 6745-6750.
3. Ambroise, C. and McLachlan, G.J. (2002) Selection bias in gene extraction on the basis of microarray gene-expression data. Proc. Natl Acad. Sci. USA, 99, 6562-6566.
4. Antoniadis, A. et al. (2003) Effective dimension reduction methods for tumor classification using gene expression data. Bioinformatics, 19, 1-8.
5. Antonov, A.V. et al. (2004) Optimization models for cancer classification: extracting gene interaction information from microarray expression data. Bioinformatics, 20, 644-652.
6. Bae, K. and Mallick, B.K. (2004) Gene selection using a two-level hierarchical Bayesian model. Bioinformatics, 20, 3423-3430.
7. Ben-Dor, A. et al. (2000) Tissue classification with gene expression profiles. J. Comput. Biol., 7, 559-583.
8. Bo, T. and Jonassen, I. (2002) New feature subset selection procedures for classification of expression profiles. Genome Biol., 3, 1-17.
9. Brown, P.J. et al. (1998) Multivariate Bayesian variable selection and prediction. J.R. Stat. Soc. B, 60, 627-641.
10. Dettling, M. (2004) BagBoosting for tumor classification with gene expression data. Bioinformatics, 20, 3583-3593.
11. Dettling, M. and Büuhlmann, P. (2003) Boosting for tumor classification with gene expression data. Bioinformatics, 19, 1061-1069.
12. Devroye, L. (1986) Non-Uniform Random Variate Generation. Springer, New York.
13. Dougherty, E.R. (2001) Small sample issues for microarray-based classification. Comp. Funct. Genomics, 2, 28-34.
14. Dudoit, Y. et al. (2002) Comparison of discrimination methods for the classification of tumors using gene expression data. J. Am. Stat. Assoc., 97, 77-87.
15. Furey, T. et al. (2000) Support vector machine classification and validation of cancer tissue samples using microarray expression data. Bioinformatics, 16, 906-914.
16. George, E.I. and McCulloch, R.E. (1993) Variable selection via Gibbs sampling. J. Am. Stat. Assoc., 88, 881-889.
17. Geman, S. and Geman, D. (1984) Stochastic relaxation, Gibbs distribution, and the Bayesian restoration of images. IEEE Trans. Pattern Anal. Mach. Intell., 6, 721-741.
18. Gilks, W. et al. (1996) Markov Chain Monte Carlo in Practise. Chapman and Hall, London.
19. Golub, T.R. et al. (1999) Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science, 286, 531-537.
20. Gupta, M. and Ibrahim, J.G. (2007) Variable selection in regression mixture modeling for the discovery of gene regulatory networks. J. Am. Stat. Assoc., 102, 867-880.
21. Hedelfank, I. et al. (2001) Gene expression profiles in hereditary breast cancer. N. Eng. J. Med., 344, 539-548.
22. Jaeger, J. et al. (2003) Improved gene selection for classification of microarrays. Pac. Symp. Biocomput., 8, 53-64.
23. Khan, J. et al. (2001) Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks. Nat. Med., 7, 673-679.
24. Keely, P. et al. (1998) Integrins and GTPases in tumour cell growth, motility and invasion. Trends Cell Biol., 8, 101-107.
25. Lachenbruch, P.A. and Mickey, M.R. (1968) Estimation of error rates in discriminant analysis. Technometrics, 10, 1-11.
26. Lamnisos, D. et al. (2009) Transdimensional sampling algorithms for Bayesian variable selection in classification problems with many more variables than observations. J. Comput. Graph. Stat., 18, 592-612.
27. Le CaoK.-A. and Chabrier, P. (2008) ofw: an R package to selection continuous variables for multiclass classification with a stochastic wrapper method. J. Stat. Softw., 28, 1-16.
28. Le Cao K.-A. et al. (2008) A sparse PLS for variable selection when integrating omics data. Stat. Appl. Genet. Mol. Biol., 7, Article 35.
29. Lee, K.E. et al. (2003) Gene selection: a Bayesian variable selection approach. Bioinformatics, 19, 90-97.
30. Li, Y. et al. (2002) Bayesian automatic relevance determination algorithms for classifying gene expression data. Bioinformatics, 18, 1332-1339.
31. Ma, S. et al. (2007) Supervised group Lasso with applications to microarray data analysis. BMC Bioinformatics, 8, 1471-2105.
32. Maccalma, T. et al. (1996) Molecular characterization of human zyxin. J. Biol. Chem., 271, 31470-31478.
33. McLachlan, G.J. (1992) Discriminant Analysis and Statistical Pattern Recognition. Wiley, New York, p. 342.
34. McLachlan, G.J. et al. (2004) Analyzing Microarray Gene Expression Data. Wiley, Hoboken, New Jersey.
35. McLachlan, G.J. et al. (2008) Correcting for selection bias via cross-validation in the classification of microarray data. In Balakrishnan, N. et al. (eds.) Beyond Parametrics in Interdisciplinary Research: Festschrift in Honour of Professor Pranab K. Sen., Vol. 1. IMS Collections, Hayward, CA, pp. 383-395.
36. Nguyen, D.V. and Rocke, D.M. (2002) Tumor classification by partial least squares using microarray gene expression data. Bioinformatics, 18, 39-50.
37. Notterman, D. et al. (2001) Transcriptional gene expression profiles of colorectal adenoma, adenocarcinoma, and normal tissue examined by oligonucleotidearrays. Cancer Res., 61, 3124-3130.
38. Pan, W. (2002) A comparative review of statistical methods for discovering differentially expressed genes in replicated microarray experiments. Bioinformatics, 18, 546-554.
39. Panagiotelisa, A. and Smith, M. (2008) Bayesian identification, selection and estimation of semiparametric functions in high dimensional additive models. J. Econom., 143, 291-316.
40. Rocke, D.R. et al. (2009) Papers on normalization, variable selection, classification or clustering of microarray data. Bioinformatics, 25, 701-702.
41. Shailubhai, K. et al. (2000) Uroguanylin treatment suppresses polyp formation in the Apc(Min/+) mouse and induces apoptosis in human colon adenocarcinoma cells via cyclic GMP. Cancer Res., 60, 5151-5157.
42. Smith, D. et al. (1995) Antibodies against human CD63 activate transfected rat basophilic leukemia (RBL-2H3) cells. Mol. Immunol., 32, 1339-1344.
43. Sobol, R. et al. (1987) Clinical importance of myeloid antigen expression in adult acute lymphoblastic leukemia. N. Eng. J. Med., 316, 1111-1117.
44. Spizz, G. and Blackshear, P. (1997) Identification and characterization of cathepsin B as the cellular MARCKS cleaving enzyme. J. Biol. Chem., 272, 23833-23842.
45. Tibshirani, R. et al. (2002) Diagnosis of multiple cancer types by shrunken centroids of gene expression. Proc. Natl Acad. Sci. USA, 99, 6567-6572.
46. Troyanskaya, O.G. et al. (2002) Nonparametric methods for identifying differentially expressed genes in microarray data. Bioinformatics, 18, 1454-1361.
47. Wang, Y. and Gilmore, T. (2003) Zyxin and paxillin proteins: focal adhesion plaque LIM domain proteins go nuclear. Biochim. Biophys. Acta, 1593, 115-120.
48. West, M. (2000) Bayesian factor regression models in the large p small n paradigm. In Bernardo, J.M. et al. (ed.) Bayesian Statistics 7. Oxford University Press, Oxford, pp.733-742.
49. Yam, J. et al. (2001) Suppression of the tumorigenicity of mutant p53- transformed rat embryo fibroblasts through expression of a newly cloned rat nonmuscle myosin heavy chain-B. Oncogene, 20, 58-68.
50. Yeung, K.Y. et al. (2005) Bayesian model averaging: development of an improved multiclass, gene selection and classification tool for microarray data. Bioinformatics, 21, 2394-2402.
51. Zellner, A. (1986) On assessing prior distributions and Bayesian regression analysis with g-prior distributions. In Goel, P.K. and Zellner, A. (eds) Bayesian Inference and Decision Techniques: Essays in Honor of Bruno de Finetti. North-Holland, Amsterdam, pp. 233-243.