메뉴 건너뛰기




Volumn 12, Issue 11, 2008, Pages 1039-1048

Gene selection using hybrid particle swarm optimization and genetic algorithm

Author keywords

Gene selection; Genetic algorithm; Particle swarm optimization; Support vector machine

Indexed keywords

AEROSPACE APPLICATIONS; ALGORITHMS; BIOACTIVITY; BIOELECTRIC PHENOMENA; BOOLEAN FUNCTIONS; CLASSIFICATION (OF INFORMATION); DIAGNOSIS; EVOLUTIONARY ALGORITHMS; GALLIUM; GENE EXPRESSION; GENES; GENETIC ALGORITHMS; INTEGER PROGRAMMING; NUMERICAL METHODS; OPTIMIZATION; SCHEDULING ALGORITHMS; SUPPORT VECTOR MACHINES;

EID: 45749096693     PISSN: 14327643     EISSN: 14337479     Source Type: Journal    
DOI: 10.1007/s00500-007-0272-x     Document Type: Article
Times cited : (141)

References (29)
  • 1
    • 0028496468 scopus 로고
    • Learning boolean concepts in the presence of many irrelevant features
    • Almuallim H and Dietterich T (1994). Learning boolean concepts in the presence of many irrelevant features. Artif Intell 69(1-2): 279-305
    • (1994) Artif Intell , vol.69 , Issue.1-2 , pp. 279-305
    • Almuallim, H.1    Dietterich, T.2
  • 2
    • 0033536012 scopus 로고    scopus 로고
    • Broad patterns of gene expression revealed by clustering of tumor and normal colon tissues probed by oligonucleotide arrays
    • Alon U, Barkai U and Notterman DA et al (1999). Broad patterns of gene expression revealed by clustering of tumor and normal colon tissues probed by oligonucleotide arrays. Proc Natl Acad Sci 96: 6745-6750
    • (1999) Proc Natl Acad Sci , vol.96 , pp. 6745-6750
    • Alon, U.1    Barkai, U.2    Notterman, D.A.3
  • 3
    • 0033692876 scopus 로고    scopus 로고
    • Tissue classification with gene expression profiles
    • Ben-Dor A, Bruhn L and Friedman N et al (2000). Tissue classification with gene expression profiles. J Comput Biol 7: 559-583
    • (2000) J Comput Biol , vol.7 , pp. 559-583
    • Ben-Dor, A.1    Bruhn, L.2    Friedman, N.3
  • 6
    • 0033636139 scopus 로고    scopus 로고
    • Support vector machine classification and validation of cancer tissue samples using microarray expression data
    • Furey TS, Cristianini N and Duffy N et al (2000). Support vector machine classification and validation of cancer tissue samples using microarray expression data. Bioinformatics 16: 906-914
    • (2000) Bioinformatics , vol.16 , pp. 906-914
    • Furey, T.S.1    Cristianini, N.2    Duffy, N.3
  • 8
    • 0033569406 scopus 로고    scopus 로고
    • Molecular classification of cancer: Class discovery and class prediction by gene expression monitoring
    • Golub T, Slonim D and Tamayo P et al (1999). Molecular classification of cancer: Class discovery and class prediction by gene expression monitoring. Science 28: 531-537
    • (1999) Science , vol.28 , pp. 531-537
    • Golub, T.1    Slonim, D.2    Tamayo, P.3
  • 9
    • 0036161259 scopus 로고    scopus 로고
    • Gene selection for cancer classification using support vector machines
    • Guyon I, Weston J and Barnhill S et al (2002). Gene selection for cancer classification using support vector machines. Mach Learn 46: 389-422
    • (2002) Mach Learn , vol.46 , pp. 389-422
    • Guyon, I.1    Weston, J.2    Barnhill, S.3
  • 10
    • 85065703189 scopus 로고    scopus 로고
    • Correlation-based feature selection for discrete and numeric class machine learning
    • In: Morgan Kaufmann, San Francisco, CA
    • Hall M (2000) Correlation-based feature selection for discrete and numeric class machine learning. In: 17th International Conference on Machine Learning. Morgan Kaufmann, San Francisco, CA
    • (2000) 17th International Conference on Machine Learning
    • Hall, M.1
  • 11
    • 10244276949 scopus 로고    scopus 로고
    • A spline function approach for detecting differentially expressed genes in microarray data analysis
    • He W (2004). A spline function approach for detecting differentially expressed genes in microarray data analysis. Bioinformatics 20: 2954-2963
    • (2004) Bioinformatics , vol.20 , pp. 2954-2963
    • He, W.1
  • 14
    • 0035664710 scopus 로고    scopus 로고
    • Gene assessment and sample classification for gene expression data using a genetic algorithm/k-nearest neighbor method
    • Li L, Darden TA and Weingberg CR et al (2001a). 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
    • (2001) Comb Chem High Throughput Screen , vol.4 , pp. 727-739
    • Li, L.1    Darden, T.A.2    Weingberg, C.R.3
  • 15
    • 0036139278 scopus 로고    scopus 로고
    • Gene selection for sample classification based on gene expression data: Study of sensitivity to choice of parameters of the GA/KNN method
    • Li L, Weinberg CR and Darden TA et al (2001b). Gene selection for sample classification based on gene expression data: Study of sensitivity to choice of parameters of the GA/KNN method. Bioinformatics 17: 1131-1142
    • (2001) Bioinformatics , vol.17 , pp. 1131-1142
    • Li, L.1    Weinberg, C.R.2    Darden, T.A.3
  • 16
    • 0037245772 scopus 로고    scopus 로고
    • Genetic algorithms applied to multi-class prediction for the analysis of gene expression data
    • Ooi CH and Tan P (2003). Genetic algorithms applied to multi-class prediction for the analysis of gene expression data. Bioinformatics 19: 37-44
    • (2003) Bioinformatics , vol.19 , pp. 37-44
    • Ooi, C.H.1    Tan, P.2
  • 17
    • 0035999977 scopus 로고    scopus 로고
    • A comparative review of statistical methods for discovering differentially expressed genes in replicated Microarray experiments
    • Pan W (2002). A comparative review of statistical methods for discovering differentially expressed genes in replicated Microarray experiments. Bioinformatics 18: 546-554
    • (2002) Bioinformatics , vol.18 , pp. 546-554
    • Pan, W.1
  • 18
    • 0344872506 scopus 로고    scopus 로고
    • Molecular classification of cancer types from microarray data using the combination of genetic algorithms and support vector machines
    • Peng S, Xu Q and Ling XB 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
    • (2003) FEBS Lett , vol.555 , pp. 358-362
    • Peng, S.1    Xu, Q.2    Ling, X.B.3
  • 19
    • 33748416594 scopus 로고    scopus 로고
    • Incremental wrapper-based gene selection from microarray data for cancer classification
    • Ruiz R, Riquelme JC and Aguilar-Ruiz JS (2006). Incremental wrapper-based gene selection from microarray data for cancer classification. Pattern Recognit 39(12): 2383-2392
    • (2006) Pattern Recognit , vol.39 , Issue.12 , pp. 2383-2392
    • Ruiz, R.1    Riquelme, J.C.2    Aguilar-Ruiz, J.S.3
  • 20
    • 33847127850 scopus 로고    scopus 로고
    • A combination of modified particle swarm optimization algorithm and support vector machine for gene selection and tumor classification
    • Shen Q, Shi WM and Kong W et al (2007). A combination of modified particle swarm optimization algorithm and support vector machine for gene selection and tumor classification. Talanta 71: 1679-1683
    • (2007) Talanta , vol.71 , pp. 1679-1683
    • Shen, Q.1    Shi, W.M.2    Kong, W.3
  • 21
    • 84901407037 scopus 로고    scopus 로고
    • Hybrid evolutionary algorithms based on pso and ga
    • In: Sarker R, Reynolds R, Abbass H et al (eds)
    • Shi XH, Lu YH, Zhou CG et al (2003) Hybrid evolutionary algorithms based on pso and ga. In: Sarker R, Reynolds R, Abbass H et al (eds) Proceeding of IEEE Congress on Evolutionary computation, pp 2393-2399
    • (2003) Proceeding of IEEE Congress on Evolutionary Computation , pp. 2393-2399
    • Shi, X.H.1    Lu, Y.H.2    Zhou, C.G.3
  • 22
    • 0034911875 scopus 로고    scopus 로고
    • An efficient and robust statistical modeling approach to discover differentially expressed genes using genomic expression profiles
    • Thomas JG, Olson JM and Tapscott SJ et al (2001). An efficient and robust statistical modeling approach to discover differentially expressed genes using genomic expression profiles. Genome Res 11: 1227-1236
    • (2001) Genome Res , vol.11 , pp. 1227-1236
    • Thomas, J.G.1    Olson, J.M.2    Tapscott, S.J.3
  • 23
    • 33646353175 scopus 로고    scopus 로고
    • The challenges of gene expression microarrays for the study of human cancer
    • Tinker AV, Boussioutas A and Bowtell DDL (2006). The challenges of gene expression microarrays for the study of human cancer. Cancer Cell 9: 333-339
    • (2006) Cancer Cell , vol.9 , pp. 333-339
    • Tinker, A.V.1    Boussioutas, A.2    Bowtell, D.D.L.3
  • 24
    • 0036856209 scopus 로고    scopus 로고
    • Nonparametric methods for identifying differentially expressed genes in microarray data
    • Troyanskaya OG, Garber ME and Brown PO et al (2002). Nonparametric methods for identifying differentially expressed genes in microarray data. Bioinformatics 18: 1454-1461
    • (2002) Bioinformatics , vol.18 , pp. 1454-1461
    • Troyanskaya, O.G.1    Garber, M.E.2    Brown, P.O.3
  • 26
    • 0035949684 scopus 로고    scopus 로고
    • Predicting the clinical status of human breast cancer using gene expression profiles
    • West M, Blanchette C and Dressman H et al (2001). Predicting the clinical status of human breast cancer using gene expression profiles. Proc Natl Acad Sci 98: 11462-11467
    • (2001) Proc Natl Acad Sci , vol.98 , pp. 11462-11467
    • West, M.1    Blanchette, C.2    Dressman, H.3
  • 28
    • 25144492516 scopus 로고    scopus 로고
    • Efficient feature selection via analysis of relevance and redundancy
    • Yu L and Liu H (2004). Efficient feature selection via analysis of relevance and redundancy. J Mach Learn Res 5: 1205-1224
    • (2004) J Mach Learn Res , vol.5 , pp. 1205-1224
    • Yu, L.1    Liu, H.2
  • 29
    • 30344438839 scopus 로고    scopus 로고
    • Gene selection using support vector machines with non-convex penalty
    • Zhang H, Ahn J and Lin X et al (2005). Gene selection using support vector machines with non-convex penalty. Bioinformatics 22: 88-95
    • (2005) Bioinformatics , vol.22 , pp. 88-95
    • Zhang, H.1    Ahn, J.2    Lin, X.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.