ESVM: Evolutionary support vector machine for automatic feature selection and classification of microarray data

BioSystems

Volumn 90, Issue 2, 2007, Pages 516-528

  Huang, Hui Ling ^a   Chang, Fang Lin ^b  

^a JINWEN UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Taiwan)

^b TRI SERVICE GENERAL HOSPITAL   (Taiwan)

Author keywords

Classification; Gene expression; Intelligent genetic algorithm; Microarray data analysis; Support vector machine

Indexed keywords

CLASSIFICATION; DATA PROCESSING; DATA SET; GENE EXPRESSION; GENETIC ALGORITHM; MODEL VALIDATION; OPTIMIZATION;

ARTICLE; AUTOANALYSIS; CLASSIFIER; CONTROLLED STUDY; DATA ANALYSIS; DATA BASE; DIAGNOSTIC ACCURACY; DIAGNOSTIC VALUE; EPIDEMIOLOGY; EVOLUTIONARY ADAPTATION; FREQUENCY ANALYSIS; GENE IDENTIFICATION; GENETIC ALGORITHM; GENETIC ANALYSIS; GENETIC SELECTION; HUMAN; HUMAN TISSUE; MATHEMATICAL MODEL; MICROARRAY ANALYSIS; NUCLEOTIDE SEQUENCE; PARAMETER; VALIDATION STUDY;

EID: 34548379115     PISSN: 03032647     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.biosystems.2006.12.003     Document Type: Article

References (37)

1. Armstrong S.A., Staunton J.E., Silverman L.B., Pieters R., den Boer M.L., Minden M.D., Sallan S.E., Lander E.S., Golub T.R., and Korsmeyer S.J. MLL translocations specify a distinct gene expression profile that distinguishes a unique leukaemia. Nat. Genet. 30 1 (2002) 41-47
2. Bhattacharjee A., Richards W.G., Staunton J., Li C., Monti S., Vasa P., Ladd C., Beheshti J., Bueno R., Gillette M., Loda M., Weber G., Mark E.J., Lander E.S., Wong W., Johnson B.E., Golub T.R., Sugarbaker D.J., and Meyerson M. Classification of human lung carcinomas by mRNA expression profiling reveals distinct adenocarcinoma subclasses. Proc. Natl. Acad. Sci. U.S.A. 98 24 (2001) 13790-13795
3. Bradford J.R., and Westhead D.R. Improved prediction of protein-protein binding sites using a support vector machines approach. Bioinformatics 21 8 (2005) 1487-1494
4. Chang, C.-C., Lin, C.-J., 2003. LIBSVM: a library for support vector machines. Software available at http://www.csie.ntu.edu.tw/∼cjlin/libsvm.
5. Cortes C., and Vapnik V. Support-vector network. Mach. Learn. 20 (1995) 273-297
6. Detting M., and Buhlmann P. Boosting for tumor classification with gene expression data. Bioinformatics 19 9 (2003) 1061-1069
7. Ding C. Unsupervised feature selection via two-way ordering in gene expression analysis. Bioinformatics 19 10 (2003) 1259-1266
8. Furey T.S., Cristianini N., Duffy N., Bednarski D.W., Schummer M., and Haussler D. Support vector machine classification and validation of cancer tissue samples using microarray expression data. Bioinformatics 16 10 (2000) 906-914
9. Golub T.R., Slonim D.K., Tamayo P., Huard C., Gaasenbeek M., Mesirov J.P., Coller H.M., Loh L., Downing J.R., Caligiuri M.A., Bloomfield C.D., and Lander E.S. Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 286 (1999) 531-537
10. Guo J., Chen H., Sun Z., and Lin Y. A novel method for protein secondary structure prediction using dual-layer SVM and profiles. Proteins 54 4 (2004) 738-743
11. Ho S.-Y., Liu C.-C., and Liu S. Design of an optimal nearest neighbor classifier using an intelligent genetic algorithm. Pattern Recogn. Lett. 23 (2002) 1495-1503
12. Ho S.-Y., Shu L.-S., and Chen J.-H. Intelligent evolutionary algorithms for large parameter optimization problems. IEEE Trans. Evolutionary Comput. 8 6 (2004) 522-541
13. Ho S.-Y., Hsieh C.-H., Chen H.-M., and Huang H.-L. Interpretable gene expression classifier with an accurate and compact fuzzy rule base for microarray data analysis. BioSystems 85 (2006) 165-176
14. Hsu C.W., and Lin C.J. A simple decomposition method for support vector machines. Mach. Learn. 46 (2002) 291-314
15. Khan J., Wei J.S., Ringner M., Saal L.H., Ladanyi M., Westermann F., Berthold F., Schwab M., Antonescu C.R., Peterson C., and Meltzer P.S. Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks. Nat. Med. 7 6 (2001) 658-659
16. Li H., and Jiang T. A class of edit kernels for SVMs to predict translation initiation sites in eukaryotic mRNAs. J. Comput. Biol. 12 6 (2005) 702-718
17. Li W., and Yang Y. How many genes are needed for a discriminant microarray data analysis. In: Lin S.M., and Johnson K.F. (Eds). Methods of Microarray Data Analysis (2002), Kluwer Academic 137-150
18. Li L., Clarice R., Darden T.A., and Pedersen L.G. Gene selection for sample classification based on gene expression data: study of sensitivity to choice of parameters of the GA/KNN method. Bioinformatics 17 12 (2001) 1131-1142
19. Mierswa I. Evolutionary learning with kernels: a generic solution for large margin problems. Proceedings of the GECCO'06. Washington, USA (2006) 1553-1560
20. Nutt C.L., Mani D.R., Betensky R.A., Tamayo P., Cairncross J.G., Ladd C., Pohl U., Hartmann C., McLaughlin M.E., Batchelor T.T., Black P.M., Deimling A.V., Pomeroy S.L., Golub T.R., and Louis D.N. Gene expression-based classification of malignant gliomas correlates better with survival than histological classification. Cancer Res. 63 7 (2003) 1602-1607
21. Ooi C.H., and Tan P. Genetic algorithms applied to multi-class prediction for the analysis of gene expression data. Bioinformatics 19 1 (2003) 37-44
22. Parkinson H., Sarkans U., Shojatalab M., Abeygunawardena N., Contrino S., Coulson R., Farne A., Garcia Lara G., Holloway E., Kapushesky M., Lilja P., Mukherjee G., Oezcimen A., Rayner T., Rocca-Serra P., Sharma A., Sansone S., and Brazma A. ArrayExpress-a public repository for microarray gene expression data at the EBI. Nucleic Acids Res. 31 1 (2003) 68-71
23. Peng S., Xu Q., Ling X.B., Peng X., Du W., and Chen L. Molecular classification of cancer types from microarray data using the combination of genetic algorithms and support vector machines. FEBS Lett. 555 2 (2003) 358-362
24. Pomeroy S.L., Tamayo P., Gaasenbeek M., Sturla L.M., Angelo M., McLaughlin M.E., Kim J.Y., Goumnerova L.C., Black P.M., Lau C., Allen J.C., Zagzag D., Olson J.M., Curran T., Wetmore C., Biegel J.A., Poggio T., Mukherjee S., Rifkin R., Califano A., Stolovitzky G., Louis D.N., Mesirov J.P., Lander E.S., and Golub T.R. Prediction of central nervous system embryonal tumor outcome based on gene expression. Nature 415 6870 (2002) 436-442
25. Ramaswamy S., Tamayo P., Rifkin R., Mukherjee S., Yeang C.-H., Angelo M., Ladd C., Reich M.L., Latulippe E., Mesirov J.P., Poggio T., Gerald W., Loda M., Lander E.S., and Golub T.R. Multiclass cancer diagnosis using tumor gene expression signatures. Proc. Natl. Acad. Sci. U.S.A. 98 26 (2001) 15149-15154
26. 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., and Golub T.R. Diffuse large B-cell lymphoma outcome prediction by gene expression profiling and supervised machine learning. Nat. Med. 8 1 (2002) 68-74
27. Singh D., Febbo P., Ross K., Jackson D., Manola J., Ladd C., Tamayo P., Renshaw A., D'Amico A., Richie J., Lander E., Loda M., Kantoff P., Golub T., and Sellers W. Gene expression correlates of clinical prostate cancer behavior. Cancer Cell 1 (2002) 203-209
28. Statnikov A., Aliferis C.F., Tsamardinos I., Hardin D., and Levy S. A comprehensive evaluation of multicategory classification methods for microarray gene expression cancer diagnosis. Bioinformatics 21 5 (2005) 631-643
29. Staunton J.E., Slonim D.K., Coller H.A., Tamayo P., Angelo M.J., Park J., Scherf U., Lee J.K., Reinhold W.O., Weinstein J.N., Mesirov J.P., Lander E.S., and Golub T.R. Chemosensitivity prediction by transcriptional profiling. Proc. Natl. Acad. Sci. U.S.A. 98 19 (2001) 10787-10792
30. Su A.I., et al. Molecular classification of human carcinomas by use of gene expression signatures. Cancer Res. 61 20 (2001) 7388-7393
31. Vinterbo S.A., Kim E.Y., and Ohno-Machado L. Small, fuzzy and interpretable gene expression based classifiers. Bioinformatics 21 19 (2005) 1964-1970
32. Ward J.J., McGuffin L.J., Buxton B.F., and Jones D.T. Secondary structure prediction with support vector machines. Bioinformatics 19 13 (2003) 1650-1655
33. Yeung K.Y., Bumgarner R.E., and Raftery A.E. Bayesian model averaging: development of an improved multiclass, gene selection and classification tool for microarray data. Bioinformatics 21 10 (2005) 2394-2402
34. Zhang S.W., Pan Q., Zhang H.C., Zhang Y.L., and Wang H.Y. Classification of protein quaternary structure with support vector machine. Bioinformatics 19 18 (2003) 2390-2396
35. Zhang Q., Yoon S., and Welsh W.J. Improved method for predicting β-turn using support vector machine. Bioinformatics 21 10 (2005) 2370-2374
36. Zhao Y., Pinilla C., Valmori D., Martin R., and Simon R. Application of support vector machines for T-cell epitopes prediction. Bioinformatics 19 15 (2003) 1978-1984
37. Zhou X., and Mao K.Z. LS Bound based gene selection for DNA microarray data. Bioinformatics 21 8 (2005) 1559-1564