Regression Approaches for Microarray Data Analysis

Journal of Computational Biology

Volumn 10, Issue 6, 2003, Pages 961-980

  Segal, Mark R ^a   Dahlquist, Kam D ^b   Conklin, Bruce R ^b  

^a UNIVERSITY OF CALIFORNIA   (United States)

^b UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

Cardiomyopathy; Gene harvesting; Least angle regression; Microarray; Support vector machine

Indexed keywords

ANALYTIC METHOD; ARTICLE; ARTIFACT; CARDIOMYOPATHY; DATA ANALYSIS; DNA MICROARRAY; EVALUATION; GENETIC ANALYSIS; MODEL; NUCLEOTIDE SEQUENCE; PRIORITY JOURNAL; REGRESSION ANALYSIS; TRANSGENIC MOUSE;

ANIMALS; CARDIOMYOPATHIES; GENE EXPRESSION PROFILING; MICE; MICE, TRANSGENIC; OLIGONUCLEOTIDE ARRAY SEQUENCE ANALYSIS; REGRESSION ANALYSIS;

MUS MUSCULUS;

EID: 0742321914     PISSN: 10665277     EISSN: None     Source Type: Journal    
DOI: 10.1089/106652703322756177     Document Type: Article

References (40)

1. Akaike, H. 1973. Information theory and an extension of the maximum likelihood principle. Second Int. Symp. Info. Theory, 267-281.
2. Breiman, L. 2001a. Statistical modeling: The two cultures. Statist. Sci. 16, 199-215.
3. Breiman, L. 2001b. Random forests. Machine Learning 45, 5-32.
4. Breiman, L., Friedman, J.H., Olshen, R.A., and Stone, C.J. 1984. Classification and Regression Trees, Wadsworth, Belmont, CA.
5. Brown, M.P., Grundy, W.N., Lin, D., Cristianini, N., Sugnet, C.W., Furey, T.S., Ares, M., and Haussler, D. 2000. Knowledge-based analysis of microarray gene expression data by using support vector machines. PNAS 97, 262-267.
6. Cristianini, N., and Shawe-Taylor, J. 2000. An Introduction to Support Vector Machines, Cambridge University Press, Cambridge.
7. Dudoit, S., Fridyland, J., and Speed, T.P. 2002. Comparison of discrimination methods for the classification of tumors using gene expression data. J. Amer. Statist. Assoc. 97, 77-87.
8. Efron, B., Hastie, T.J., Johnstone, I., and Tibshirani, R.J. 2002. Least angle regression. Submitted.
9. Eisen, M.B., Spellman, P.T., Brown, P.O., and Botstein, D. 1988. Cluster analysis and display of genome-wide expression patterns. PNAS 95, 14863-14868.
10. Friedman, J.H., 1991. Multivariate adaptive regression splines. Ann. Statist. 19, 1-67.
11. Furey, T.S., Cristianini, N., Duffy, N., Bednarski, D.W., Schummer, M., and Haussler, D. 2000. Support vector machine classification and validation of cancer tissue samples using microarray expression data. Bioinformatics 16, 906-914.
12. Golub, T.R., Slonim, D.K., Tamayo, P., Huard, C., Gaasenbeek, M., Mesirov, J.P., Coller, H., Loh, M.L., Downing, J.R., Caligiuri, M.A., Bloomfield, C.D., and Lander, E.S. 1999. Molecular classification of cancer: Class discovery and class prediction by gene expression monitoring. Science 286, 531-537.
13. Goryachev, A.B., Macgregor, P.F., and Edwards, A.M. 2001. Unfolding of microarray data. J. Comp. Biol. 8, 443-461.
14. Guardavaccaro, D., Ciotti, M.T., Schafer, B.W., Montagnoli, A., and Tirone, F. 1995. Inhibition of differentiation in myoblasts deprived of the interferon-related protein PC4. Cell Growth Differ. 6, 159-169.
15. Guyon, I., Weston, J., Barnhill, S., and Vapnik, V. 2002. Gene selection for cancer classification using support vector machines. Machine Learning 46, 389-422.
16. Hastie, T., Tibshirani, R., Botstein, D., and Brown, P. 2001a. Supervised harvesting of expression trees. Genome Biol. 2, 0003.1-0003.12.
17. Hastie, T., Tibshirani, R., and Friedman, J.H. 2001b. The Elements of Statistical Learning, Springer-Verlag, New York.
18. Hoerl, A.E., and Kennard, R.W. 1970. Ridge regression: Biased estimation for nonorthogonal problems. Technometrics 12, 55-67.
19. Keleş, S., van der Laan, M., and Eisen, M.B. 2002. Identification of regulatory elements using a feature selection algorithm. Submitted.
20. Kent, C., and Carman, G.M. 1999. Interactions among pathways for phosphadidylcholine metabolism, CTP synthesis and secretion through the Golgi apparatus. Trends Biochem. Sci. 24, 127-162.
21. Kim, S., Dougherty, E.R., Barrera, J., Chen, Y., Bittner, M.L., and Trent, J.M. 2002. Strong feature sets from small samples. J. Comp. Biol. 9, 127-146
22. Kooperberg, C., Fazzio, T.G., Delrow, J.J., and Tsukiyama, T. 2002. Improved background correction for spotted microarrays. J. Comp. Biol. 9, 55-66.
23. Lee, Y., and Lee, C.-K. 2002. Classification of multiple cancer types by multicategory support vector machines using gene expression data. Submitted.
24. Li, C., and Wong, W.H. 2001. Model-based analysis of oligonucleotide arrays: Expression index computation and outlier detection. PNAS 98, 31-36.
25. Li, H., and Hong, F. 2001. Cluster-Rasch models for microarray gene expression data. Genome Biol. 2, 0031.1-0031.13
26. p. Technometrics 15, 661-675.
27. Osborne, M., Presnell, B., and Turlach, B. 2000. On the LASSO and its dual. J. Comp. Graph. Statist. 9, 319-337.
28. Nelson, D.P., Wechsler, S.B., Miura, T., Stagg, A., Newburger, J.W., Mayer, J.E., and Neufeld, E.J. 2002. Myocardial immediate early gene activation after cardiopulmonary bypass with cardiac ischemia-reperfusion. Ann. Thorac. Surg. 73, 156-162.
29. Newton, M.A., Kendziorski, C.M., Richmond, C.S., Blattner, F.R., and Tsui, K.W. 2001. On differential variability of expression ratios: Improving statistical inference about gene expression changes from microarray data. J. Comp. Biol. 8, 37-52
30. Redfern, C.H., Coward, P., Degtyarev, M.Y., Lee, E.K., Kwa, A.T., Hennighausen, L., Bujard, H., Fishman, G.I., and Conklin, B.R. 1999. Conditional expression and signaling of a specifically designed Gi-coupled receptor in transgenic mice. Nat. Biotechnol. 17, 165-169.
31. Redfern, C.H., Degtyarev, M.Y., Kwa, A.T., Salomonis, N., Cotte, N., Nanevicz, T., Fidelman, N., Desai, K., Vranizan, K., Lee, E.K., Coward, P., Shah, N., Warrington, J.A., Fishman, G.I., Bernstein, D., Baker, A.J., and Conklin, B.R. 2000. Conditional expression of a Gi-coupled receptor causes ventricular conduction delay and a lethal cardiomy-opathy. PNAS 97, 4826-4831.
32. Schwartz, G. 1979. Estimating the dimension of a model. Ann. Statist. 6, 461-464.
33. Tibshirani, R. 1996. Regression shrinkage and selection via the lasso. J. Roy. Statist. Soc. B. 58, 267-288.
34. Tibshirani, R., and Knight, K. 1999. The covariance inflation criterion for adaptive model selection. J. Roy. Statist. Soc. B. 61, 529-546.
35. Tusher, V., Tibshirani, R., and Chu, G. 2001. Significance analysis of microarrays applied to the ionizing radiation response. PNAS 98, 5116-5121.
36. Vapnik, V. 1998. Statistical Learning Theory, Wiley, New York.
37. West, M., Blanchette, C., Dressman, H., Huang, E., Ishida, S., Spang, R., Zuzan, H., Marks, J., and Nevins, J. 2001. Predicting the clinical status of human breast cancer using gene expression profiles. PNAS 98, 11462-11467.
38. Westphal, C.H., Muller, L., Zhou, A., Zhu, X., Bonner-Weir, S., Schambelan, M., Steiner, D.F., Lindberg, I., and Leder, P. 1999. The neuroendocrine protein 7B2 is required for peptide hormone processing in vivo and provides a novel mechanism for pituitary Cushing's disease. Cell 96, 689-700.
39. Ye, J. 1998. On measuring and correcting the effects of data mining and model selection. J. Am. Statist. Assoc. 93, 120-131.
40. Zhang, H., Yu, C.-Y., Singer, B., and Xiong, M. 2001. Recursive partitioning for tumor classification with gene expression microarray data. PNAS 98, 6730-6735.