MSVM-RFE: Extensions of SVM-RFE for multiclass gene selection on DNA microarray data

Bioinformatics

Volumn 23, Issue 9, 2007, Pages 1106-1114

  Zhou, Xin ^a   Tuck, David P ^a  

^a YALE UNIVERSITY SCHOOL OF MEDICINE   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ACCURACY; ARTICLE; BIOINFORMATICS; BLADDER CARCINOMA; BREAST ADENOCARCINOMA; CLASSIFICATION ALGORITHM; COLORECTAL CARCINOMA; CONTROLLED STUDY; DNA MICROARRAY; ENDOMETRIUM CARCINOMA; GENE EXPRESSION; GENE IDENTIFICATION; KIDNEY CARCINOMA; LUNG ADENOCARCINOMA; LYMPHOMA; MELANOMA; OVARY ADENOCARCINOMA; PANCREAS ADENOCARCINOMA; PERFORMANCE; PLEURA MESOTHELIOMA; PRIORITY JOURNAL; PROSTATE ADENOCARCINOMA; SUPPORT VECTOR MACHINE; ALGORITHM; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; COMPUTER PROGRAM; GENE EXPRESSION PROFILING; METHODOLOGY;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; GENE EXPRESSION PROFILING; OLIGONUCLEOTIDE ARRAY SEQUENCE ANALYSIS; PATTERN RECOGNITION, AUTOMATED; SOFTWARE;

EID: 34249855141     PISSN: 13674803     EISSN: 13674811     Source Type: Journal    
DOI: 10.1093/bioinformatics/btm036     Document Type: Article

References (48)

1. Alon,U. et al. (1999) Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays. Pro. Nat Acad. Sci., 96, 6745-6750.
2. Ambroise,C. and McLachlan,G.J. (2002) Selection bias in gene extraction on the basis of microarray gene-expression data. Pro. Nat Acad. Sci. 99, 6562-6566.
3. Armstrong,S.A. et al. (2002) MLL translocations specify a distinct gene expression profile that distinguishes a unique leukemia. Nat. Genet., 30, 41-47.
4. Bhattacharjee,A. et al. (2001) Classification of human lung carcinomas by mrna expression profiling reveals distinct adenocarcinoma subclasses. Pro. Nat Acad. Sci., 98, 13790-13795.
5. Bottou,L. et al. (1994) Comparison of classifier methods: A case study in handwriting digit recognition. In Proceedings of International Conference on Pattern Recognition, Vol. 2, IEEE Computer Society Press, Los Alamitos, CA, pp. 77-82.
6. Breiman,L. et al. (1984) Classification and Regression Trees Wadsworth and Brooks, Monterey, CA.
7. Brown,M.P.S. et al. (2000) Knowledge-based analysis of microarray gene expression data by using support vector machines. Pro. Nat Acad. Sci., 97, 262-267.
8. Chai,H. and Domeniconi,C. (2004) An evaluation of gene selection methods for multi-class microarray data classification. In Scheffer.T (ed.) Proceedings of the Second European Workshop on Data Mining and Texi Mining in Bioinformatics, pp. 3-10.
9. Chang,C.-C. and Lin,C.-J. (2001) LIBSVM: A library for support vector machines. Software available at http://www.csie.ntu.edu.tw/~cjlin/ libsvm.
10. Crammer,K. and Singer,Y. (2001) On the algorithmic implementation of multiclass kernel-based vector machines. J. Mach. Learn. Res., 2, 265-292.
11. Das,R. et al. (2006) Computational prediction of methylation status in human genomic sequences. Pro. Nat Acad. Sci., 103, 10713-10716.
12. Dudoit,S. et al. (2002) Comparison of discrimination methods for the classification of tumors using gene expression data. J. Am. Stat. Assoc., 97, 77-87.
13. Frank,O. et al. (2006) Gene expression signature of primary imatinib-resistant chronic myeloid leukemia patients. Leukemia, 20, 1400-1407.
14. Furey,T. et al. (2000) Support vector machine classification and validation of cancer tissue samples using microarray expression data. Bioinformatics, 16, 906-914.
15. Geoffrion,A.M. (1968) Proper efficiency and the theory of vector maximization. J. Math. Anal. Appl., 22, 618-630.
16. Gertz,E.M. and Wright,S.J. (2003) Object-oriented software for quadratic programming. ACM T. Math. Software, 29, 58-81.
17. Gesierich,S. et al. (2005) Colocalization of the tetraspanins, CO-029 and CD151, with integrins in human pancreatic adenocarcinoma: Impact on cell motility. Clinical Cancer Res., 11, 2840-2852.
18. Golub,T. et al. (1999) Molecular classification of cancer: Class discovery and class prediction by gene expression monitoring. Science, 286, 531-537.
19. Guyon,I. et al. (2002) Gene selection for cancer classification using support vector machines. Mach. Learn., 46, 389-422.
20. Hewitt,K.J. et al. (2006) The claudin gene family: Expression in normal and neoplastic tissues. BMC Cancer, 6, 186.
21. Hilario,M. et al. (2006) Processing and classification of protein mass spectra. Mass Spectrom. Rev., 25, 0277-7037.
22. Hsu,C.-W. and Lin,C.-J. (2002) A comparison of methods for multiclass support vector machines. IEEE T. Neural Networ., 13, 415-425.
23. Ideo,H. et al. (2005) Galectin-4 binds to sulfated glycosphingolipids and carcinoembryonic antigen in patches on the cell surface of human colon adenocarcinoma cells. J. Biol. Chem., 280, 4730-4737.
24. Kohavi,R. (1995). A study of cross-validation and bootstrap for accuracy estimation and model selection. In Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence, Morgan X. Zhou and D P. TuckKaufmann Publishers, San Francisco, pp. 1137-1145.
25. Kreßel,U.H.-G. (1999) Pairwise classification and support vector machines. In Schölkopf,B. and Burges,C.J.C. and Smola,A.J. (eds.) Advances in Kernel Methods: Support Vector Learning, MIT press, Cambridge, MA, USA. pp. 255-268.
26. LeCun,Y. et al. (1990) Optimal brain damage. In Touretzky,D.S. (ed.) Advances in Neural Information Processing Systems II, Morgan Kaufmann Publishers, San Mateo,CA, pp. 598-605.
27. Lee,K.E. et al. (2003) Gene selection: A Bayesian variable selection approach. Bioinformatics, 19, 90-97.
28. Lee,Y. et al. (2004) Multicategory support vector machines: Theory and application to the classification of microarray data and satellite radiance data. J. Am. Stat. Assoc., 99, 67-81.
29. Li,T. et al. (2004) A comparative study of feature selection and multiclass classification methods for tissue classification based on gene expression. Bioinformatics, 20, 2429-2437.
30. Mukherjee,S. (2003) Classifying microarray data using support vector machines. In Berrar,D.P. Dubitzky,W. and Granzow,M. (eds.) A Practical Approach to Microarray Data Analysis, Kluwer Academic Publishers, Boston, MA, pp. 166-185.
31. Oh,D.S. et al. (2006) Estrogen-regulated genes predict survival in hormone receptor-positive breast cancers. J. Clin. Oncol., 24 1656-1664.
32. Poggio,T. et al. (2002) b. In J.Winkler and M.Niranjan, editors, Uncertainty in Geometric Computations, Kluwer Academic Publishers, Norwell, MA, pp. 131-141.
33. Ramaswamy,S. et al. (2001) Multiclass cancer diagnosis using tumor gene expression signatures. Pro. Nat. Acad. Sci., 98, 15149-15154.
34. Rifkin,R. et al. (2003) An analytical method for multiclass molecular cancer classification. SIAM Review, 45, 706-723.
35. Ross,D.T. et al. (2000) Systematic variation in gene expression patterns in human cancer cell. Nat. Genet., 24, 227-235.
36. Statnikov,A. et al. (2005) A comprehensive evaluation of multicategory classification methods for microarray gene expression cancer diagnosis. Bioinformatics, 21, 631-643.
37. Staunton,J.E. et al. (2001) Chemosensitivity prediction by transcriptional profiling. Pro. Nat. Acad. Sci., 98, 10787-10792.
38. Su,A.I. et al. (2001) Molecular classification of human carcinomas by use of gene expression signatures. Cancer Res., 61, 7388-7393.
39. Tibshirani,R. et al. (2002) Diagnosis of multiple cancer types by shrunken centroids of gene expression. Proc. Nat. Acad. Sci., 99, 6567-6572.
40. Vapnik,V.N. (1998) Statistical Learning Theory\/. John Wiley and Sons, New York.
41. West,M. et al. (2001) Predicting the clinical status of human breast cancer by using gene expression profiles. Proc. Nat. Acad. Sci., 98, 11462-11467.
42. Weston, J. and Watkins,C. (1999) Support vector machines for multiclass pattern recognition. In Proceedings of the Seventh European Symposium On Artificial Neural Networks.
43. Yeang,C.-H. et al. (2001) Molecular classification of multiple tumor types. Bioinformatics, 17(Suppl. 1), S316-S322.
44. Zhang,H.H. et al. (2006) Gene selection using support vector machines with non-convex penalty. Bioinformatics, 22, 88-95.
45. Zheng,C. et al. (2006) Gene expression profiling of CD34+ cells identifies a molecular signature of chronic myeloid leukemia blast crisis. Leukemia, 20, 1028-1034.
46. Zhou,X. and Mao,K.Z. (2005a) Gene selection of DNA microarray data based on Regularization Networks. In Gallagher,M. Hogan.J.M. and Maire,F (eds.) IDEAL, Vol 3578 of Lecture Notes in Computer Science, Springer, pp. 414-421.
47. Zhou,X. and Mao,K.Z. (2005b) LS Bound based gene selection for DNA microarray data. Bioinformatics, 21, 1559-1564.
48. Zhou,X. and Mao,K.Z. (2006) The ties problem resulting from counting-based error estimators and its impact on gene selection algorithms. Bioinformatics, 22, 2507-2515.