Multiclass cancer classification and biomarker discovery using GA-based algorithms

Bioinformatics

Volumn 21, Issue 11, 2005, Pages 2691-2697

  Liu, Jane Jijun ^a   Cutler, Gene ^a   Li, Wuxiong ^a   Pan, Zheng ^a   Peng, Sihua ^b   Hoey, Tim ^a   Chen, Liangbiao ^c   Ling, Xuefeng Bruce ^a  

^a AMGEN INC   (United States)

^b ZHEJIANG UNIVERSITY   (China)

^c INSTITUTE OF GEOLOGY AND GEOPHYSICS   (China)

Author keywords

[No Author keywords available]

Indexed keywords

ALPHA FETOPROTEIN; BOMBESIN; CA 125 ANTIGEN; CA 15-3 ANTIGEN; CA 19-9 ANTIGEN; CHORIONIC GONADOTROPIN; COMPLEMENTARY DNA; PROSTASIN; PROSTATE SPECIFIC ANTIGEN;

ARTICLE; CANCER CLASSIFICATION; CANCER SCREENING; COMPUTER PROGRAM; CONTROLLED STUDY; DNA MICROARRAY; GENE EXPRESSION PROFILING; GENETIC ALGORITHM; HIGH THROUGHPUT SCREENING; HUMAN; HUMAN CELL; INTERMETHOD COMPARISON; PRIORITY JOURNAL; PROTEIN LOCALIZATION;

EID: 20744448405     PISSN: 13674803     EISSN: 13674811     Source Type: Journal    
DOI: 10.1093/bioinformatics/bti419     Document Type: Article

References (33)

1. Ben-Hur,A. and Brutlag,D. (2003) Remote homology detection: a motif based approach. Bioinformatics, 19 (Suppl. 1), i26-i33.
2. Brawer,M.K. (2000) Prostate-specific antigen. Semin. Surg. Oncol., 18, 3-9.
3. Brown,M.P. et al. (2000) Knowledge-based analysis of microarray gene expression data by using support vector machines. Proc. Natl Acad. Sci. USA, 97, 262-267.
4. Chen,Z. et al. (2005) GoPipe: streamlined Gene Ontology annotation for batch anonymous sequences with statistics. Prog. Biochem. Biophys., 32, 187-191.
5. Dhanasekaran,S.M. et al. (2001) Delineation of prognostic biomarkers in prostate cancer. Nature, 412, 822-826.
6. Diamandis,E.P. (2004) Mass spectrometry as a diagnostic and a cancer biomarker discovery tool: opportunities and potential limitations. Mol. Cell Proteom., 3, 367-378.
7. Furey, T.S. et al. (2000) Support vector machine classification and validation of cancer tissue samples using microarray expression data. Bioinformatics, 16, 906-914.
8. Goldberg,D.E. (1989) Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley, New York.
9. Goldberg,D.E. and Deb,K. (1991) A comparative analysis of selection schemes used in genetic algorithms. In Rawlins,G. (ed.), Foundations of Genetic Algorithms. Morgan Kaufman, Berlin, pp. 69-93.
10. Golub,T.R. et al. (1999) Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science, 286, 531-537.
11. Harris,M.A. et al. (2004) The Gene Ontology (GO) database and informatics resource. Nucleic Acids Res., 32 (Database issue), D258-D261.
12. Hastie,T. Tibshirani,R. and Friedman,J. (2001) The Elements of Statisitcal Learning Data Mining, Inference, and Prediction. Springer, New York, pp. 453-480.
13. Jaakkola,T. et al. (1999) Using the Fisher kernel method to detect remote protein homologies. Proc. Int. Conf. Intell. Syst. Mol. Biol., 149-158.
14. Kim,J.H. et al. (2002) Osteopontin as a potential diagnostic biomarker for ovarian cancer. JAMA, 287, 1671-1679.
15. Li,L. et al. (2001) Gene assessment and sample classification for gene expression data using a genetic algorithm/k-nearest neighbor method. Comb. Chem. High Throughput Screen, 4, 727-739.
16. Miklos,G.L. and Maleszka,R. (2004) Microarray reality checks in the context of a complex disease. Nat. Biotechnol., 22, 615-621.
17. Mok,S.C. et al. (2001) Prostasin, a potential serum marker for ovarian cancer: identification through microarray technology. J. Natl Cancer Inst., 93, 1458-1464.
18. Munagala,K. et al. (2004) Cancer characterization and feature set extraction by discriminative margin clustering. BMC Bioinform., 5, 21.
19. Ooi,C.H. and Tan,P. (2003) Genetic algorithms applied to multi-class prediction for the analysis of gene expression data. Bioinformatics, 19, 37-44.
20. Peng,S. et al. (2003) Molecular classification of cancer types from microarray data using the combination of genetic algorithms and support vector machines. FEBS Lett., 555, 358-362.
21. Petricoin,E.F. et al. (2002) Use of proteomic patterns in serum to identify ovarian cancer. Lancet, 359, 572-577.
22. Pouliot,Y. et al. (2001) DLAN: a novel algorithm for genome ontological classification. Genome Res., 11, 1766-1779.
23. Ramaswamy,S. et al. (2001) Multiclass cancer diagnosis using tumor gene expression signatures. Proc. Natl Acad. Sci. USA, 98, 15149-15154.
24. Ross,D.T. et al. (2000) Systematic variation in gene expression patterns in human cancer cell lines. Nat. Genet., 24, 227-235.
25. Rubin,M.A. et al. (2002) alpha-Methylacyl coenzyme A racemase as a tissue biomarker for prostate cancer. JAMA, 287, 1662-1670.
26. Su,A.I. et al. (2001) Molecular classification of human carcinomas by use of gene expression signatures. Cancer Biol., 61, 7388-7393.
27. Tibshirani,R. et al. (2002) Diagnosis of multiple cancer types by shrunken centroids of gene expression. Proc. Natl Acad. Sci. USA, 99, 6567-6572.
28. Tibshirani,R. et al. (2003) Class prediction by nearest shrunken centroids, with applications to DNA microarrays. Statist. Sci., 104-117.
29. Vapnik,V. (1998). Statistical Learning Theory. Wiley.
30. Welsh,J.B. et al. (2003) Large-scale delineation of secreted protein biomarkers overexpressed in cancer tissue and serum. Proc. Natl Acad. Sci. USA, 100, 3410-3415.
31. Yeang,C.H. et al. (2001) Molecular classification of multiple tumor types. Bioinformatics, 17 (Suppl. 1), S316-S322.
32. Zhou,W. et al. (1998) Identifying markers for pancreatic cancer by gene expression analysis. Cancer Epidemiol. Biomarkers Prev., 7, 109-112.
33. Zien,A. et al. (2000) Engineering support vector machine kernels that recognize translation initiation sites. Bioinformatics, 16, 799-807.