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Volumn , Issue , 2005, Pages 453-460

Extraction of informative genes from microarray data

Author keywords

Classification of cancer data; Gene expression; Gene subset selection; Informative genes; K nearest neighbor classifier; Probabilistic model building genetic algorithm; Support vector machine; Weighted fitness

Indexed keywords

PATHOLOGY; PROBABILITY; SIGNAL TO NOISE RATIO; TISSUE; TUMORS; VECTORS;

EID: 32444445435     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1068009.1068081     Document Type: Conference Paper
Times cited : (23)

References (25)
  • 1
    • 3543106611 scopus 로고    scopus 로고
    • Classification of gene expression profile using combinatory method of evolutionary computation and machine learning
    • S. Ando and H. Iba. Classification of gene expression profile using combinatory method of evolutionary computation and machine learning. Genetic Programming and Evolvable Machines, 5:145-156, 2004.
    • (2004) Genetic Programming and Evolvable Machines , vol.5 , pp. 145-156
    • Ando, S.1    Iba, H.2
  • 2
    • 0003984832 scopus 로고
    • Population-based incremental learning: A method for integrating genetic search based function optimization and competitive learning
    • Carnegie Mellon University, Pittsburgh, Pennsylvania
    • S. Baluja. Population-based incremental learning: A method for integrating genetic search based function optimization and competitive learning. Technical Report CMU-CS-94-163, Carnegie Mellon University, Pittsburgh, Pennsylvania, 1994.
    • (1994) Technical Report , vol.CMU-CS-94-163
    • Baluja, S.1
  • 6
    • 0345724886 scopus 로고    scopus 로고
    • Reliable classification of two-class cancer data using evolutionary algorithms
    • K. Deb and A. R. Reddy. Reliable classification of two-class cancer data using evolutionary algorithms. BioSystems, 72:111-129, 2003.
    • (2003) BioSystems , vol.72 , pp. 111-129
    • Deb, K.1    Reddy, A.R.2
  • 10
    • 0036161259 scopus 로고    scopus 로고
    • Gene selection for cancer classification using support vector machine
    • I. Guyon, J. Weston, S. Barnhill, and V. Vapnik. Gene selection for cancer classification using support vector machine. Machine Learning, 46(1-3):389-422, 2002.
    • (2002) Machine Learning , vol.46 , Issue.1-3 , pp. 389-422
    • Guyon, I.1    Weston, J.2    Barnhill, S.3    Vapnik, V.4
  • 15
    • 84958959530 scopus 로고    scopus 로고
    • From recombination of genes to the estimation of distribution I. Binary parameters
    • Lecture Notes in Computer Science (LNCS) 1411, Springer-Verlag, Berlin, Germany
    • H. Mühlenbein and G. Paaß. From recombination of genes to the estimation of distribution I. Binary parameters. In Parallel Problem Solving from Nature-PPSN IV, Lecture Notes in Computer Science (LNCS) 1411, pages 178-187. Springer-Verlag, Berlin, Germany, 1996.
    • (1996) Parallel Problem Solving from Nature-PPSN IV , pp. 178-187
    • Mühlenbein, H.1    Paaß, G.2
  • 17
    • 4344623174 scopus 로고    scopus 로고
    • Linear and combinatorial optimizations by estimation of distribution algorithms
    • IPSJ
    • T. Paul and H. Iba. Linear and combinatorial optimizations by estimation of distribution algorithms. In Proceedings of the 9th MPS Symposium on Evolutionary Computation, pages 99-106. IPSJ, 2003. Article available at http://www.iba.k.u-tokyo.ac.jp/english/EDA.htm.
    • (2003) Proceedings of the 9th MPS Symposium on Evolutionary Computation , pp. 99-106
    • Paul, T.1    Iba, H.2
  • 18
    • 35248847014 scopus 로고    scopus 로고
    • Reinforcement learning estimation of distribution algorithm
    • Lecture Notes in Computer Science (LNCS) 2724, Springer-Verlag
    • T. Paul and H. Iba. Reinforcement learning estimation of distribution algorithm. In Proceedings of GECCO2003, Lecture Notes in Computer Science (LNCS) 2724, pages 1259-1270. Springer-Verlag, 2003.
    • (2003) Proceedings of GECCO2003 , pp. 1259-1270
    • Paul, T.1    Iba, H.2
  • 19
    • 33749362808 scopus 로고    scopus 로고
    • Identification of informative genes for molecular classification using probabilistic model building genetic algorithm
    • Lecture Notes in Computer Science (LNCS) 3102, Springer-Verlag
    • T. Paul and H. Iba. Identification of informative genes for molecular classification using probabilistic model building genetic algorithm. In Proceedings of GECCO2004, Lecture Notes in Computer Science (LNCS) 3102, pages 414-425. Springer-Verlag, 2004.
    • (2004) Proceedings of GECCO2004 , pp. 414-425
    • Paul, T.1    Iba, H.2
  • 20
    • 4344650509 scopus 로고    scopus 로고
    • Selection of the most useful subset of genes for gene expression-based classification
    • Portland, Oregon, USA
    • T. Paul and H. Iba. Selection of the most useful subset of genes for gene expression-based classification. In Proceedings of the 2004 Congress on Evolutionary Computation (CEC2004), pages 2076-2083, Portland, Oregon, USA, 2004.
    • (2004) Proceedings of the 2004 Congress on Evolutionary Computation (CEC2004) , pp. 2076-2083
    • Paul, T.1    Iba, H.2
  • 21
    • 0003654346 scopus 로고    scopus 로고
    • A survey of optimizations by building and using probabilistic models
    • Illigal Report 99018, Illinois Genetic Algorithms Laboratory, University of Illinois at Urbana-Champaign, USA
    • M. Pelikan, D. Goldberg, and F. Lobo. A survey of optimizations by building and using probabilistic models. Technical Report, Illigal Report 99018, Illinois Genetic Algorithms Laboratory, University of Illinois at Urbana-Champaign, USA, 1999.
    • (1999) Technical Report
    • Pelikan, M.1    Goldberg, D.2    Lobo, F.3
  • 23
    • 0003983441 scopus 로고    scopus 로고
    • Oxford University Press, New York, USA
    • M. Schena. DNA Microarrays. Oxford University Press, New York, USA, 2000.
    • (2000) DNA Microarrays
    • Schena, M.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.