A robust hybrid between genetic algorithm and support vector machine for extracting an optimal feature gene subset

Genomics

Volumn 85, Issue 1, 2005, Pages 16-23

  Li, Li ^a,b   Jiang, Wei ^a   Li, Xia ^a,b,c,f   Moser, Kathy L ^d   Guo, Zheng ^a,b,c   Du, Lei ^a   Wang, Qiuju ^e   Topol, Eric J ^f   Wang, Qing ^f   Rao, Shaoqi ^a,e,f  

^a HARBIN MEDICAL UNIVERSITY   (China)

^b TONGJI UNIVERSITY   (China)

^c HARBIN INSTITUTE OF TECHNOLOGY   (China)

^d UNIVERSITY OF MINNESOTA   (United States)

^e CHINESE PLA GENERAL HOSPITAL   (China)

^f LERNER RESEARCH INSTITUTE   (United States)

Author keywords

Feature gene selection; Genetic algorithm; Microarray; Support vector machine

Indexed keywords

ALGORITHM; ARTICLE; B CELL LYMPHOMA; CONTROLLED STUDY; CORRELATION ANALYSIS; DNA EXTRACTION; DNA MICROARRAY; GENE EXPRESSION; GENE IDENTIFICATION; GENETIC ANALYSIS; HUMAN; HUMAN CELL; HYBRIDIZATION; MOLECULAR DYNAMICS; PHENOTYPE; PRIORITY JOURNAL; SIGNAL NOISE RATIO;

ALGORITHMS; GENE EXPRESSION PROFILING; GENE EXPRESSION REGULATION, LEUKEMIC; HUMANS; LYMPHOMA, B-CELL; MODELS, GENETIC; OLIGONUCLEOTIDE ARRAY SEQUENCE ANALYSIS;

EID: 20844445085     PISSN: 08887543     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ygeno.2004.09.007     Document Type: Article

References (37)

1. M. Schena, D. Shalon, R.W. Davis, and P.O. Brown Quantitative monitoring of gene expression patterns with a complementary DNA microarray Science 270 1995 467 470
2. U. Alon 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
3. R. Kohavi, and G.H. John Wrappers for feature subset selection Artif. Intell. 97 1997 273 324
4. I. Tsamardinos, and C.F. Aliferis Towards principled feature selection: relevance, filters and wrappers, Ninth International Workshop on Artificial Intelligence and Statistics 2003 Key West FL
5. P.J. Park, M. Pagano, and M. Bonetti A nonparametric scoring algorithm for identifying informative genes from microarray data Pac. Symp. Biocomput. 2001 52 63
6. L. Li, X. Li, and Z. Guo Efficiency of two filters for feature gene selection Life Sci. Res. 7 2003 372 396 In Chinese
7. E.P. Xing, M.I. Jordan, and R.M. Karp Feature selection for high-dimensional genomic microarray data, Machine Learning: Proceedings of the Eighteenth International Conference, San Francisco 2001 Morgan Kaufmann San Mateo, CA
8. A.L. Blum, and P. Langley Selection of relevant features and examples in machine learning Artif. Intell. 97 1997 245 271
9. M.J. Brusco An enhanced branch-and-bound algorithm for a partitioning problem Br. J. Math. Stat. Psychol. 56 2003 83 92
10. R.C. Holte Combinatorial auctions, knapsack problems, and hill-climbing search E. Stroulia S. Matwin Proceedings of the 14th Biennial Conference of the Canadian Society on Computational Studies of Intelligence: Advances in Artificial Intelligence 2001 Springer Ottawa, ON 57 66
11. C.R. Houck, J.A. Joines, M.G. Kay, and J.R. Wilson Empirical investigation of the benefits of partial Lamarckianism Evol. Comput. 5 1997 31 60
12. F.M. Stefanini, and A. Camussi The reduction of large molecular profiles to informative components using a genetic algorithm Bioinformatics 16 2000 923 931
13. L. Li, T.A. Darden, C.R. Weinberg, A.J. Levine, and L.G. Pedersen Gene assessment and sample classification for gene expression data using a genetic algorithm/k-nearest neighbor method Comb. Chem. High Throughput Screen. 4 2001 727 739
14. M. Hall Correlation-Based Feature Selection for Machine Learning, Department of Computer Science, University of Waikato 1998 Hamilton New Zealand
15. S.J. Cho, and M.A. Hermsmeier Genetic algorithm guided selection: variable selection and subset selection J. Chem. Inf. Comput. Sci. 42 2002 927 936
16. M.P. Brown Knowledge-based analysis of microarray gene expression data by using support vector machines Proc. Natl. Acad. Sci. USA 97 2000 262 267
17. C.Z. Cai, W.L. Wang, L.Z. Sun, and Y.Z. Chen Protein function classification via support vector machine approach Math. Biosci. 185 2003 111 122
18. S. Hua, and Z. Sun A novel method of protein secondary structure prediction with high segment overlap measure: support vector machine approach J. Mol. Biol. 308 2001 397 407
19. T.S. Furey Support vector machine classification and validation of cancer tissue samples using microarray expression data Bioinformatics 16 2000 906 914
20. A.A. Alizadeh Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling Nature 403 2000 503 511
21. O. Troyanskaya Missing value estimation methods for DNA microarrays Bioinformatics 17 2001 520 525
22. N. Cristianini, and J. Shawe-Taylor An Introduction to Support Vector Machines 2000 Cambridge University Press Cambridge, UK
23. Z. Guo, X. Li, and S. Rao Analysis of Medical Data: an Introduction to Bioinformatics 2001 Harbin Pub., Harbin China
24. E.S. Gilbert The effect of unequal variance-covariance matrices on Fisher's linear discriminant function Biometrics 25 1969 505 515
25. T.R. Golub Molecular classification of cancer: class discovery and class prediction by gene expression monitoring Science 286 1999 531 537
26. L. Li, C.R. Weinberg, T.A. Darden, and L.G. Pedersen Gene selection for sample classification based on gene expression data: study of sensitivity to choice of parameters of the GA/KNN method Bioinformatics 17 2001 1131 1142
27. T.M. Cover, and P.E. Hart Nearest neighbor pattern classification IEEE Trans. Inform. Theor. 13 1967 21 27
28. M. Ashburner Gene ontology: tool for the unification of biology The Gene Ontology Consortium, Nat. Genet. 25 2000 25 29
29. R.E. Davis, K.D. Brown, U. Siebenlist, and L.M. Staudt Constitutive nuclear factor kappaB activity is required for survival of activated B cell-like diffuse large B cell lymphoma cells J. Exp. Med. 194 2001 1861 1874
30. R.F. Murphy, M.V. Boland, and M. Velliste Towards a systematics for protein subcellular location: quantitative description of protein localization patterns and automated analysis of fluorescence microscope images Proc. Int. Conf. Intell. Syst. Mol. Biol. 8 2000 251 259
31. H.B. Burke Discovering patterns in microarray data Mol. Diagn. 5 2000 349 357
32. X. Ji, J. Li-Ling, and Z. Sun Mining gene expression data using a novel approach based on hidden Markov models FEBS Lett. 542 2003 125 131
33. A. Schliep, A. Schonhuth, and C. Steinhoff Using hidden Markov models to analyze gene expression time course data Bioinformatics 19 Suppl 1 2003 i255 i263
34. A. Szabo Multivariate exploratory tools for microarray data analysis Biostatistics 4 2003 555 567
35. A. Chilingaryan, N. Gevorgyan, A. Vardanyan, D. Jones, and A. Szabo Multivariate approach for selecting sets of differentially expressed genes Math. Biosci. 176 2002 59 69
36. X.-W. Chen Gene selection for cancer classification using bootstrapped genetic algorithms and support vector machines, Proceedings of the Computational Systems Bioinformatics (CSB 2003) 2003 IEEE Stanford, CA 504 505
37. X. Li, S. Rao, Y. Wang, and B. Gong Gene mining: a novel and powerful ensemble decision approach to hunting for disease genes using microarray expression profiling Nucleic Acids Res. 32 2004 2685 2694