Cancer classification using gene expression data

Information Systems

Volumn 28, Issue 4, 2003, Pages 243-268

  Lu, Ying ^a   Han, Jiawei ^a  

^a University of Illinois at Urbana Champaign   (United States)

Author keywords

Cancer classification; Gene expression data

Indexed keywords

ALGORITHMS; ARTIFICIAL INTELLIGENCE; DECISION THEORY; DNA SEQUENCES; GENES; NEURAL NETWORKS; ONCOLOGY; RECURSIVE FUNCTIONS; STATISTICAL METHODS;

ASYMMETRICAL CLASSIFICATION ERRORS; BAYESIAN NETWORKS; CANCER CLASSIFICATION METHODS; GENE EXPRESSION DATA; GENE SELECTION METHODS; SUPPORT VECTOR MACHINE;

CLASSIFICATION (OF INFORMATION);

EID: 0037623370     PISSN: 03064379     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0306-4379(02)00072-8     Document Type: Article

References (61)

1. A. Azuaje, Interpretation of genome expression patterns: computational challenges and opportunities, IEEE Eng. Med. Biol. 1 (2000) 26-41.
2. A. Alizadeh, et al., Distinct types of diffuse large b-cell lymphoma identified by gene expression profiling, Nature 403 (2000) 503-511.
3. T.R. Golub, D.K. Slonim, P. Tamayo, M. Gaasenbeek, C. Huard, J.P. Mesirov, H. Coller, M. Loh, J.R. Downing, M.A. Caligiuri, C.D. Bloomfield, E.S. Lander, Molecular classification of cancer: class discovery and class prediction by gene expression monitoring, Science 286 (1999) 531-537.
4. L. Veer, H. Da, M. Bijver, et al., Gene expression profiling predicts clinical outcome of breast cancer, Nature 415 (2002) 530-536.
5. S. Pomeroy, P. Tamayo, M. Gassenbeek, et al., Prediction of central nervous embryonal tumour outcome based on gene expression, Nature 415 (2002) 436-442.
6. D. Zajchowski, et al., Identification of gene expression profiles that predict the aggressive behavior of breast cancer cells, Cancer Res. 61 (2001) 5168-5178.
7. T. Sorlie, et al., Gene expression patterns of breast carcinomas distinguish tumor subclass with clinical implications. Proc. Natl. Acad. Sci. USA (2001) 10869-10874.
8. W. Dubitzky, M. Granzow, D. Berrar, Comparing Symbolic and Subsymbolic Machine Learning Approaches to Classification of Cancer and Gene Indentification, Kluwer Academic, Dordrecht, 2001.
9. L. Veer, D. Jone, The microarray way to tailored cancer treatment, Nat. Med. 8 (2002) 13-14.
10. J. DeRisi, L. Penland, P. Brown, M. Bittner, P. Meltzer, M. Ray, Y. Chen, Y. Su, J. Trent, Use of a cDNA microarray to analyse gene expression patterns in human cancer, Nat. Genet. 4 (1996) 457-460.
11. D. Slonim, P. Tamayo, J. Mesirov, T. Golub, E. Lander, Class prediction and discovery using gene expression data, in: Proceedings of the Fourth International Conference on Computational Molecular Biology (RECOMB), Tokyo, Japan, 2000, pp. 263-272.
12. S. Lakhani, A. Ashworth, Microarray and histopathological analysis of tumours: the future the past? Nat. Rev. Can. 1 (2001) 151-157.
13. D. Nguyen, D. Rocke, Classification of Acute Leukemia based on DNA Microarray Gene Expressions using Partial Least Squares, Kluwer Academic, Dordrecht, 2001.
14. A. Berns, Cancer: gene expression in diagnosis, Nature 403 (2000) 491-492.
15. I. Guyon, J. Wetson, S. Barnhill, M. D., V. Vapnik, Gene selection for cancer classification using support vector machines, Mach. Learn. 46 (2000) 389-422.
16. P. Russel, Fundamentals of Genetics, Addison Wesley Longman Inc., 2000.
17. A. Ben-Dor, L. Bruhn, N. Friedman, I. Nachman, M. Schummer, Z. Yakhini, Tissue classification with gene expression profiles, in: Proceedings of the Fourth Annual International Conference on Computational Molecular Biology, Tokyo, Japan, 2000.
18. M. Schena, D. Shalon, R. Davi, P. Brown, Quantitative monitoring of gene expression patterns with a complementary DNA microarray, Science 270 (1995) 467-470.
19. D. Lockhart, H. Dong, M. Byrne, M. Follettie, M. Gallo, M. Chee, M. Mittmann, C. Wang, M. Kobayashi, H. Horton, Expression monitoring by hybridization to high-density oligonucleotide arrays, Nat. Biotechnol. 14 (1996) 1675-1680.
20. R. Fisher, The use of multiple measurements in taxomonic problems, Annu. Eugen. 7 (1936) 179-188.
21. S. Dudoit, J. Fridlyand, T. Speed, Comparison of discrimination methods for the classification of tumors using gene expression data, Technical Report, Berkeley, June 2000.
22. A. Keller, M. Schummer, L. Hood, W. Ruzzo, Bayesian classification of DNA array expression data, Technical Report, University of Washington, August 2000.
23. N. Friedman, M. Nachman, D. Pe'er, Using Bayesian networks to analyze expression data, in: Proceedings of the Fourth Annual International Conference on Computational Molecule Biology, Tokyo, Japan, 2000, pp. 127-135.
24. J. Khan, et al., Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks, Nat. Med. 7 (2001) 673-679.
25. I. Jollife, Principle Component Analysis, Springer, Berlin, 1986.
26. C. Bishop, Neural Networks for Pattern Recognition, Clarendon Press, Oxford, 1995.
27. L. Breiman, J. Friedman, A. Olshen, C. Stone, Classification and Regression Trees, Wadsworth, Belmont, CA, 1984.
28. R. Rastogi, K. Shim, Public: a decision tree classifier that integrates building and pruning, in: Proceedings of the 1996 International Conference on Very Large Databases (VLDB96), New York, 1998.
29. P. Utgoff, Incremental induction of decision trees, Mach. Learn. 4 (1989) 161-186.
30. J. Shafer, R. Agrawal, M. Mehta, Sprint: a scalable parallel classifier for data mining, in: Proceedings of the 1996 International Conference on Very Large Databases (VLDB96), Bombay, India, 1996.
31. J. Gehrke, G. Ganti, R. Ramakrishnan, W. Loh, Boat-optimistic decision tree construction, in: Proceedings of the 1999 ACM-SIGMOD International Conference on Management of Data (SIGMOD'99), Philadelphia, PA, 1999, pp. 169-180.
32. H. Zhang, C. Yu, B. Singer, M. Xiong, Recursive partitioning for tumor classification with gene expression microarray data, Proc. Natl. Acad. Sci. USA 98 (2001) 6730-6735.
33. E. Fix, J. Hodges, Discriminatory analysis, nonparametric discrimination: consistency properties, Technical Report, USAF School of Aviation Medicine, 1951.
34. U. Alon, N. Barkai, D. Notterman, K. Gish, S. Ybarra, D. Mack, A.J. Levine, 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 (1999) 6745-6750.
35. Y. Freund, R. Schapire, Large margin classification using the perceptron algorithm, in: Proceedings of the 11th Annual Conference Computational Learning Theory, Madison, WI, 1998, pp. 209-217.
36. A. Smola, P. Bartlett, B. Scholkopf, Advances in Large-Margin Classifiers, MIT Press, Cambridge, MA, 2000.
37. B. Boser, I. Guyon, V. Vapnik, A training algorithm for optimal margin classifiers, in: Proceedings of Fifth Annual ACM Workshop on Computational Learning Theory, Madison, WI, ACM Press, New York, 1992, pp. 144-152.
38. V. Vapnik, Statistical Learning Theory, Wiley, New York, NY, 1998.
39. C. Burges, A tutorial on support vector machines for pattern recognition, Data Min. Knowledge Discovery 2 (1998) 121-167.
40. M. Brown, et al., Knowledge based analysis of microarray gene expression data by using support vector machines, Proc. Natl. Acad. Sci. 97 (2000) 262-267.
41. K. Fujarewicz, M. Kimmel, J. Rzeszowska-Wolny, et al., Improved classification of gene expression data using support vector machines, J. Med. Inf. Technol. 6 (2001) M19-MI19.
42. S. Mukherjee, P. Tamayo, D. Slonim, A. Verri, T. Golub, J. Mesirov, T. Poggio, Support vector machine classification of microarray data, Technical Report, 182, MIT, CBCL, 1999.
43. N. Cristianini, J. Shawe-Taylor, An Introduction to Support Vector Machines, Cambridge University Press, Cambridge, 2000.
44. T. Furey, N. Cristianini, N. Duffy, D. Bednarski, M. Schummer, D. Haussler, Support vector machine classification and validation of cancer tissue samples using microarray expression data, Bioinformatics 16 (2000) 906-914.
45. S. Ramaswamy, P. Tamayo, R. Rifkin, et al., Multiclass cancer diagnosis using tumor gene expression signatures, PNAS 98 (26) (2001) 15149-15154.
46. J. Shawe-Taylor, N. Cristianini, Further results on the margin distribution, in: Proceedings of the 12th Annual Conference on Computational Learning Theory, New York, 1999, 278-285.
47. L. Breiman, Bagging predictors, Mach. Learn. 24 (1996) 123-140.
48. L. Breiman, Arcing classifiers, Annu. Stat. 26 (1998) 801-824.
49. Y. Freund, R. Shapire, A decision-theoretic generalization of on-line learning and application to boosting, J. Comput. System Sci. 55 (1997) 119-139.
50. R. Schapire, Y. Freund, P. Barlett, W. Lee, Boosting the margin: a new explanation for the effectiveness of voting methods, Annu. Stat. 26 (1998) 1651-1686.
51. L. Manson, P. Bartlett, J. Baxter, Direct optimization of margins improves generalization in combined classifiers, Technical Report, Australia National University, 1998.
52. V. Tresp, Scaling kernel-based systems to large data sets, Data Min. Knowledge Discovery 5(9) (2001) 197-211.
53. M. Brown, W. Grundy, D. Lin, N. Cristianni, C. Sugnet, M. Ares Jr., D. Haussler, Support vector machine classification of microarray gene expression data, Technical Report, University of California, Santa Cruz, 1999.
54. K. Crammer, Y. Singer, On the learnability and design of output codes for multiclass problems, in Proceedings of the 13th Annual Conference on Computational Learning Theory, Morgan Kaufmann, Los Altos, CA, 2000, pp. 35-46.
55. Y. Lee, Y. Lin, G. Wahba, Multicategory support vector machines, Technical Report, University of Wisconsin-Madison, 2001.
56. C. Hsu, C. Lin, A comparison on methods for multi-class support vector machines, Technical Report, National Taiwan University, Taipei, Taiwan, 2001.
57. D. Koller, M. Sahami, Towards optimal feature selection, in: Machine Learning: Proceedings of 13th International Conference, Bari, Italy, 1996.
58. W. Siedlecki, J. Sklansky, On automatic feature selection, Int. J. Pattern Recognition Artif. Intell. 2 (1988) 197-220.
59. E. Xing, M. Jordan, R. Karp, Feature selection for high-dimensional genomic microarray data, in: Proceedings of the 18th International Conference on Machine Learning, Massachusetts, USA, 2001.
60. C. Campbell, Y. Li, M. Tipping, An efficient feature selection algorithm for classification of gene expression data, in: NIPS 2001 Workshop on Machine Learning Techniques for Bioinformatics, Vancouver, Canada, December 2001.
61. J. Weston, S. Mukherjee, O. Chapelle, M. Pontil, T. Poggio, V. Vapnik, Feature selection in SVM, Adv. Neural Inf. Process. Systems 1 (2000) 668-674.