Evaluating methods for classifying expression data

Journal of Biopharmaceutical Statistics

Volumn 14, Issue 4, 2004, Pages 1065-1084

  Man, Michael Z ^a   Dyson, Greg ^b   Johnson, Kjell ^a   Liao, Birong ^c  

^a PFIZER INC   (United States)

^b ALLERGAN INC   (United States)

^c ELI LILLY AND COMPANY   (United States)

Author keywords

Biomarkers; Bootstrap cross validation; Gene approach; Leave one out cross validation; Whole genome approach

Indexed keywords

ANTILEUKEMIC AGENT; BIOLOGICAL MARKER;

ACUTE GRANULOCYTIC LEUKEMIA; ACUTE LYMPHOCYTIC LEUKEMIA; ALGORITHM; ARTICLE; ARTIFICIAL NEURAL NETWORK; CLASSIFICATION; CLINICAL TRIAL; DISCRIMINANT ANALYSIS; DNA MICROARRAY; DRUG EFFICACY; DRUG SAFETY; GENE EXPRESSION; GENOME; HUMAN; K NEAREST NEIGHBOR; PREDICTION; PRINCIPAL COMPONENT ANALYSIS; PRIORITY JOURNAL; RANDOM FOREST; REGRESSION ANALYSIS; SIMULATION; STATISTICAL ANALYSIS; SUPPORT VECTOR MACHINE; VALIDATION PROCESS;

EID: 9244261068     PISSN: 10543406     EISSN: None     Source Type: Journal    
DOI: 10.1081/BIP-200035491     Document Type: Article

References (54)

1. Aeberhard, S., De Vel, O. Y., Coomans, D. H. (2002). New fast algorithms for error rate-based stepwise variable selection in discriminant analysis. SIAM J. Sci. Comput. 22:1036-1052.
2. Alizadeh, A. A., Eisen, M. B., Davis, R. E., Ma, C., Lossos, I. S., Rosenwald, A., Boldrick, J. C., Sabet, H., Tran, T., Yu, X., Powell, J. I., Yang, L., Marti, G. E., Moore, T., Hudson, J. Jr., Lu, L., Lewis, D. B., Tibshirani, R., Sherlock, G., Chan, W. C., Greiner, T. C., Weisenburger, D. D., Armitage, J. O., Warnke, R., Staudt, L. M. (2000). Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling. Nature 403:503-511.
3. Alon, U., Barkai, N., Notterman, D. A., Gish, K., Ybarra, S., Mack, D., Levine, A. J. (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.
4. Alsberg, B. K., Douglas, B. K., Goodacre, R. (1998). Variable selection in discriminant partial least-squares analysis. Anal. Chem. 70:4126-4133.
5. Ambroise, C., 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.
6. Ben Dor, A., Bruhn, L., Friedman, N., Nachman, I., Schummer, M., Yakhini, Z. (2000). Tissue classification with gene expression profiles. J. Comput. Biol. 7:559-583.
7. Breiman, L. (2001). Random forests. Mach. Learn. 45:5-32.
8. Brown, M. P., Grundy, W. N., Lin, D., Cristianini, N., Sugnet, C. W., Furey, T. S., Ares, M. Jr., Haussler, D. (2000). Knowledge-based analysis of microarray gene expression data by using support vector machines. Proc. Natl. Acad. Sci. USA 97:262-267.
9. Bumol, T. F., Watanabe, A. M. (2001). Genetic information, genomic technologies, and the future of drug discovery. JAMA 285:551-555.
10. Cantor, C. R. (1999). Pharmacogenetics becomes pharmacogenomics: Wake up and get ready. Mol. Diagn. 4:287-288.
11. Chow, M. L., Moler, E. J., Mian, I. S. (2001). Identifying marker genes in transcription profiling data using a mixture of feature relevance experts. Physiol Genom. 5:99-111.
12. Cortijo, F. J., Perez de la Blanca, N., Molina, R., Abad, J. (1993). On the Combination of Nonparametric Nearest Neighbor Classification and Contextual Correction. Universidad de Granada Technical Report.
13. Cristianini, N., Shawe-Taylor, J. (2000). An Introduction to Support Vector Machines. Cambridge, UK: Cambridge University Press.
14. Dudoit, S., Fridlyand, J., Speed, T. P. (2002). Comparison of discrimination methods for the classification of tumors using gene expression data. J. Am. Statist. Assoc. 97:77-87.
15. Efron, B., Tibshirani, R. J. (1993). An Introduction to the Bootstrap. Boca Raton, FL: Chapman & Hall/CRC.
16. Fix, E., Hodges, J. L. (1951). Discriminatory Analysis. Nonparametric Estimation: Consistency Properties. Technical Report No. 4, Project No. 21-49-004, Randolph Field, TX: USAF School of Aviation Medicine.
17. Frank, I., Friedman, J. (1993). Statistical view of chemometric regression tools. Technometrics 35:109-135.
18. Furey, T. S., Cristianini, N., Duffy, N., Bednarski, D. W., Schummer, M., Haussler, D. (2000). Support vector machine classification and validation of cancer tissue samples using microarray expression data. Bioinformatics 16:906-914.
19. 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., Lander, E. S. (1999). Molecular classification of cancer: Class discovery and class prediction by gene expression monitoring. Science 286:531-537.
20. Gruvberger, S., Ringner, M., Chen, Y., Panavally, S., Saal, L. H., Borg, A., Ferno, M., Peterson, C., Meltzer, P. S. (2001). Estrogen receptor status in breast cancer is associated with remarkably distinct gene expression patterns. Cancer Res. 61:5979-5984.
21. Gunther, E. C., Stone, D. J., Gerwien, R. W., Bento, P., Heyes, M. P. (2003). Prediction of clinical drug efficacy by classification of drug-induced genomic expression profiles in vitro. Proc. Natl. Acad. Sci. USA 100:9608-9613.
22. Hastie, T., Tibshirani, R., Friedman, J. H. (2001). The Elements of Statistical Learning: Data Mining, Inference, and Prediction. New York: Springer.
23. Hsu, C. W., Lin, C. J. (2002). A comparison on methods for multiclass support vector machines. IEEE Trans. Neural Networks 13:415-425.
24. Ihaka, R., Gentleman, R. (1996). R: A language for data analysis and graphics. J. Comput. Graph. Statist. 5:299-314.
25. Lee, Y., Lee, C. K. (2002). Classification of Multiple Cancer Types by Multicategory Support Vector Machines Using Gene Expression Data. Technical Report 1051r ftp://ftp.stat.wisc.edu/pub/wahba/tr1051rr.ps.
26. Li, W., Yang, Y. (2002). How many genes are needed for a discriminant microarray data analysis. In: Lin, S. M., Johnson, K. F., eds. Methods of Microarray Data Analysis. Boston: Kluwer Academic, pp. 137-150.
27. Michie, D., Spiegelhalter, D. J., Taylor, C. C. (1994). Machine Learning, Neural and Statistical Classification. Michie, D., Spiegelhalter, D. J., Taylor, C. C., eds. New York: Ellis Horwood.
28. Moler, E. J., Chow, M. L., Mian, I. S. (2000). Analysis of molecular profile data using generative and discriminative methods. Physiol. Genom. 4:109-126.
29. Moreira, M., Mayoraz, E. (1998). Improved pairwise coupling classification with correcting classifiers. In: Proceedings of the Tenth European Conference on Machine Learning. London, Springer-Verlag, pp. 160-171.
30. Neter, J., Kutner, M. H., Nachtsheim, C. J., Wasserman, W. (1996). Applied Linear Statistical Models. Boston: WCB/McGraw-Hill.
31. Nguyen, D. V., Rocke, D. M. (2002). Tumor classification by partial least squares using microarray gene expression data. Bioinformatics 18:39-50.
32. Nishizuka, S., Chen, S. T., Gwadry, F. G., Alexander, J., Major, S. M., Scherf, U., Reinhold, W. C., Waltham, M., Charboneau, L., Young, L., Bussey, K. J., Kim, S., Lababidi, S., Lee, J. K., Pittaluga, S., Scudiero, D. A., Sausville, E. A., Munson, P. J., Petricoin, E. F., Liotta, L. A., Hewitt, S. M., Raffeld, M., Weinstein, J. N. (2003). Diagnostic markers that distinguish colon and ovarian adenocarcinomas: Identification by genomic, proteomic, and tissue array profiling. Cancer Res. 63:5243-5250.
33. Patterson, D. (1996). Artificial Neural Networks. Singapore: Prentice Hall.
34. Petricoin, E. F. III, Hackett, J. L., Lesko, L. J., Puri, R. K., Gutman, S. I., Chumakov, K., Woodcock, J., Feigal, D. W. Jr., Zoon, K. C., Sistare, F. D. (2002). Medical applications of microarray technologies: A regulatory science perspective. Nat. Genet. 32(Suppl):474-479.
35. Price, D., Knerr, S., Personnaz, L., Dreyfus, G. (1994). Pairwise neural network classifiers with probabilistic outputs. Neural Inf. Process. Sys. 7.
36. Ramaswamy, S., Tamayo, P., Rifkin, R., Mukherjee, S., Yeang, C. H., Angelo, M., Ladd, C., Reich, M., Latulippe, E., Mesirov, J. P., Poggio, T., Gerald, W., Loda, M., Lander, E. S., Golub, T. R. (2001). Multiclass cancer diagnosis using tumor gene expression signatures. Proc. Natl. Acad. Sci. USA 98:15149-15154.
37. Ramaswamy, S., Ross, K. N., Lander, E. S., Golub, T. R. (2003). A molecular signature of metastasis in primary solid tumors. Nat. Genet. 33:49-54.
38. Rayens, W. S. (2000). The Art of Maximizing Covariance. University of Kentucky Technical Report.
39. Ricci, M. S., El Deiry, W. S. (2000). Novel strategies for therapeutic design in molecular oncology using gene expression profiles. Curr. Opin. Mol. Ther. 2:682-690.
40. Ripley, B. D. (1996). Pattern Recognition and Neural Networks. Cambridge: Cambridge University Press.
41. Rosenblatt, F. (1958). The perceptron: A probabilistic model for information storage and organization in the brain. Psychol. Rev. 65:386-408.
42. Ross, D. T., Scherf, U., Eisen, M. B., Perou, C. M., Rees, C., Spellman, P., Iyer, V., Jeffrey, S. S., van de, R. M., Waltham, M., Pergamenschikov, A., Lee, J. C., Lashkari, D., Shalon, D., Myers, T. G., Weinstein, J. N., Botstein, D., Brown, P. O. (2000). Systematic variation in gene expression patterns in human cancer cell lines. Nat. Genet. 24:227-235.
43. Saaksjarvi, E., Khalighi, M., Minkkinen, P. (1989). Waste water pollution modeling in the southern area of lake Saimaa, Finland, by the SIMCA pattern recognition model. Chemomet Intell Lab Sys. 7:171-180.
44. Scott, D. W. (1992). Multivariate Density Estimation: Theory, Practice, and Visualization. New York: John Wiley.
45. Selaru, F. M., Xu, Y., Yin, J., Zou, T., Liu, T. C., Mori, Y., Abraham, J. M., Sato, F., Wang, S., Twigg, C., Olaru, A., Shustova, V., Leytin, A., Hytiroglou, P., Shibata, D., Harpaz, N., Meltzer, S. J. (2002). Artificial neural networks distinguish among subtypes of neoplastic colorectal lesions. Gastroenterology 122:606-613.
46. Shipp, M. A., Ross, K. N., Tamayo, P., Weng, A. P., Kutok, J. L., Aguiar, R. C., Gaasenbeek, M., Angelo, M., Reich, M., Pinkus, G. S., Ray, T. S., Koval, M. A., Last, K. W., Norton, A., Lister, T. A., Mesirov, J., Neuberg, D. S., Lander, E. S., Aster, J. C., Golub, T. R. (2002). Diffuse large B-cell lymphoma outcome prediction by gene-expression profiling and supervised machine learning. Nat. Med. 8:68-74.
47. Stone, M., Brooks, R. J. (1990). Continuum regression: Cross-validated sequentially constructed prediction embracing ordinary least squares, partial least squares, and principal component regression. J. R. Statist. Soc. Ser. B. 52:237-269.
48. Stratowa, C., Loffler, G., Lichter, P., Stilgenbauer, S., Haberl, P., Schweifer, N., Dohner, H., Wilgenbus, K. K. (2001). CDNA microarray gene expression analysis of B-cell chronic lymphocytic leukemia proposes potential new prognostic markers involved in lymphocyte trafficking. Int. J. Cancer 91:474-480.
49. Vapnik, V. (1998). Statistical Learning Theory. New York: Wiley-Interscience.
50. Wilkinson, D. G. (1994). Situ Hybridization. A practical Approach. Oxford: Oxford University Press.
51. Wold, S. (1995). PLS for multivariate linear modeling. In: Han van de Waterbeemd, ed. Chemometric Methods in Molecular Design. Weinheim: VCH Verlagsgesellschaft mbH, pp. 195-218.
52. Wu, B., Abbott, T., Fishman, D., McMurray, W., Mor, G., Stone, K., Ward, D., Williams, K., Zhao, H. (2003). Comparison of statistical methods for classification of ovarian cancer using mass spectrometry data. Bioinformatics 19:1636-1643.
53. Xiong, M., Jin, L., Li, W., Boerwinkle, E. (2000). Computational methods for gene expression-based tumor classification. Biotechniques 29:1264-1268, 1270.
54. Yeang, C. H., Ramaswamy, S., Tamayo, P., Mukherjee, S., Rifkin, R. M., Angelo, M., Reich, M., Lander, E., Mesirov, J., Golub, T. (2001). Molecular classification of multiple tumor types. Bioinformatics 17(Suppl 1):S316-S322.