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Volumn 980, Issue , 2002, Pages 41-64

Pattern recognition techniques in microarray data analysis: A survey

Author keywords

Data mining; Expression level data; Microarray; Neural networks; Pattern recognition; Statistical analysis

Indexed keywords

ARTIFICIAL NEURAL NETWORK; BAYES THEOREM; BIOINFORMATICS; BIOLOGY; CALCULATION; CLUSTER ANALYSIS; CONFERENCE PAPER; DATA ANALYSIS; DATA BASE; DNA MICROARRAY; GENE CLUSTER; GENE EXPRESSION; GENE MAPPING; GENE SEQUENCE; GENETIC ALGORITHM; PATTERN RECOGNITION; PRINCIPAL COMPONENT ANALYSIS; PROBABILITY;

EID: 0036975148     PISSN: 00778923     EISSN: None     Source Type: Book Series    
DOI: 10.1111/j.1749-6632.2002.tb04888.x     Document Type: Conference Paper
Times cited : (125)

References (95)
  • 1
    • 0035895505 scopus 로고    scopus 로고
    • The sequence of the human genome
    • VENTER, J.C., et al. 2001. The sequence of the human genome. Science 291: 1304-1351.
    • (2001) Science , vol.291 , pp. 1304-1351
    • Venter, J.C.1
  • 2
    • 0000818999 scopus 로고
    • General nature of the genetic code for proteins
    • CRICK, F.H.C., et al. 1961. General nature of the genetic code for proteins. Nature 192: 1227-1232.
    • (1961) Nature , vol.192 , pp. 1227-1232
    • Crick, F.H.C.1
  • 3
    • 36949088975 scopus 로고
    • An unstable intermediate carrying information from genes to ribosomes for protein synthesis
    • BRENNER, S., et al. 1961. An unstable intermediate carrying information from genes to ribosomes for protein synthesis. Nature 190: 576-581.
    • (1961) Nature , vol.190 , pp. 576-581
    • Brenner, S.1
  • 4
    • 0036493201 scopus 로고    scopus 로고
    • Nucleotides of transcription factor binding sites exert interdependent effects on the binding affinities of transcription factors
    • BULYK, M.L., P.L. JOHNSON & G.M. CHURCH. 2002. Nucleotides of transcription factor binding sites exert interdependent effects on the binding affinities of transcription factors. Nucleic Acids Res. 30(5): 1255-1261.
    • (2002) Nucleic Acids Res. , vol.30 , Issue.5 , pp. 1255-1261
    • Bulyk, M.L.1    Johnson, P.L.2    Church, G.M.3
  • 5
    • 2042437650 scopus 로고    scopus 로고
    • Initial sequencing and analysis of the human genome
    • LANDER, E.S., et al. 2001 Initial sequencing and analysis of the human genome. Nature 409: 860-921.
    • (2001) Nature , vol.409 , pp. 860-921
    • Lander, E.S.1
  • 6
    • 0036130619 scopus 로고    scopus 로고
    • Variable selection and pattern recognition with gene expression data generated by the microarray technology
    • SZABO, A., et al. 2002. Variable selection and pattern recognition with gene expression data generated by the microarray technology. Math. Biosci. 176(1): 71-98.
    • (2002) Math. Biosci. , vol.176 , Issue.1 , pp. 71-98
    • Szabo, A.1
  • 8
    • 0034948896 scopus 로고    scopus 로고
    • A Bayesian framework for the analysis of microarray expression data: Regularized t-test and statistical inference of gene changes
    • BALDI, P., et al. 2001. A Bayesian framework for the analysis of microarray expression data: regularized t-test and statistical inference of gene changes. Bioinformatics 17(6): 509-519.
    • (2001) Bioinformatics , vol.17 , Issue.6 , pp. 509-519
    • Baldi, P.1
  • 9
    • 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(4): 546-554.
    • (2002) Bioinformatics , vol.18 , Issue.4 , pp. 546-554
    • Pan, W.1
  • 12
    • 0034730140 scopus 로고    scopus 로고
    • Singular value decomposition for genome-wide expression data processing and modeling
    • ALTER, O., et al. 2000. Singular value decomposition for genome-wide expression data processing and modeling. Proc. Natl. Acad. Sci. USA 97: 10101-10106.
    • (2000) Proc. Natl. Acad. Sci. USA , vol.97 , pp. 10101-10106
    • Alter, O.1
  • 13
    • 0035852785 scopus 로고    scopus 로고
    • Dynamic modeling of gene expression data
    • HOLTER, N.S., et al. 2001. Dynamic modeling of gene expression data. Proc. Natl. Acad. Sci. USA 98: 1693-1698.
    • (2001) Proc. Natl. Acad. Sci. USA , vol.98 , pp. 1693-1698
    • Holter, N.S.1
  • 14
    • 0034682504 scopus 로고    scopus 로고
    • Fundamental patterns underlying gene expression profiles: Simplicity from complexity
    • HOLTER, N.S. 2000. Fundamental patterns underlying gene expression profiles: simplicity from complexity. Proc. Natl. Acad. Sci. USA 97: 8409-8414.
    • (2000) Proc. Natl. Acad. Sci. USA , vol.97 , pp. 8409-8414
    • Holter, N.S.1
  • 15
    • 0033657261 scopus 로고    scopus 로고
    • Principal components analysis to summarize microarray experiments: Application to sporulation time series
    • RAYCHAUDHURI, S., J.M. STUART & R.B. ALTMAN. 2000. Principal components analysis to summarize microarray experiments: application to sporulation time series. Pacific Symposium on Biocomputing. 455-466.
    • (2000) Pacific Symposium on Biocomputing , pp. 455-466
    • Raychaudhuri, S.1    Stuart, J.M.2    Altman, R.B.3
  • 16
    • 0029387830 scopus 로고
    • Neural networks for full-scale protein sequence classification: Sequence encoding with singular value decomposition
    • WU, C., M. BERRY, et al. 1995. Neural networks for full-scale protein sequence classification: sequence encoding with singular value decomposition. Machine Learning 21: 177-193.
    • (1995) Machine Learning , vol.21 , pp. 177-193
    • Wu, C.1    Berry, M.2
  • 18
    • 0034714135 scopus 로고    scopus 로고
    • A Bayesian system integrating expression data with sequence patterns for localizing proteins: Comprehensive application to the yeast genome
    • DRAWID, A. & M. GERSTEIN. 2000. A Bayesian system integrating expression data with sequence patterns for localizing proteins: comprehensive application to the yeast genome. J. Mol. Biol. 301: 1059-1075.
    • (2000) J. Mol. Biol. , vol.301 , pp. 1059-1075
    • Drawid, A.1    Gerstein, M.2
  • 19
    • 0035860541 scopus 로고    scopus 로고
    • Blazing pathways through genetic mountains
    • GIFFORD, D.K. 2001. Blazing pathways through genetic mountains. Science 293: 2049-2051.
    • (2001) Science , vol.293 , pp. 2049-2051
    • Gifford, D.K.1
  • 20
    • 0035221560 scopus 로고    scopus 로고
    • Using graphical models and genomic expression data to statistically validate models of genetic regulatory networks
    • HARTEMINK, A.J., D.K. GIFFORD, et al. 2001. Using graphical models and genomic expression data to statistically validate models of genetic regulatory networks. Pacific Symposium on Biocomputing. 422-433.
    • (2001) Pacific Symposium on Biocomputing , pp. 422-433
    • Hartemink, A.J.1    Gifford, D.K.2
  • 21
    • 18144442687 scopus 로고    scopus 로고
    • Inferring subnetworks from perturbed expression profiles
    • PE'ER, D., A. REGEV, G. ELIDAN & N. FRIEDMAN. 2001. Inferring subnetworks from perturbed expression profiles. Bioinformatics 17(Suppl. 1): S215-S224.
    • (2001) Bioinformatics , vol.17 , Issue.SUPPL. 1
    • Pe'er, D.1    Regev, A.2    Elidan, G.3    Friedman, N.4
  • 22
    • 0004140078 scopus 로고    scopus 로고
    • Technical Report: 01-03-02, Department of Computer Science and Engineering, University of Washington
    • FASULO, D. 1999. An analysis of recent work on clustering algorithms. Technical Report: 01-03-02, Department of Computer Science and Engineering, University of Washington.
    • (1999) An Analysis of Recent Work on Clustering Algorithms
    • Fasulo, D.1
  • 23
    • 0035375137 scopus 로고    scopus 로고
    • Computational analysis of microarray data
    • QUACKENBUSH, J. 2001. Computational analysis of microarray data. Nature Genetics 2: 418-427.
    • (2001) Nature Genetics , vol.2 , pp. 418-427
    • Quackenbush, J.1
  • 24
    • 0033027794 scopus 로고    scopus 로고
    • Interpreting patterns of gene expression with self-organizing maps: Methods and application to hematopoietic differentiation
    • TAMAYO, P., et al. 1999. Interpreting patterns of gene expression with self-organizing maps: methods and application to hematopoietic differentiation. Proc. Natl. Acad. Sci. USA 96: 2907-2912.
    • (1999) Proc. Natl. Acad. Sci. USA , vol.96 , pp. 2907-2912
    • Tamayo, P.1
  • 25
    • 0036083376 scopus 로고    scopus 로고
    • Unsupervised technique for robust target separation and analysis of DNA microarray spots through adaptive pixel clustering
    • BOZINOV, D. 2002. Unsupervised technique for robust target separation and analysis of DNA microarray spots through adaptive pixel clustering. Bioinformatics 18(5): 747-756.
    • (2002) Bioinformatics , vol.18 , Issue.5 , pp. 747-756
    • Bozinov, D.1
  • 26
    • 0031915896 scopus 로고    scopus 로고
    • Large-scale temporal gene expression mapping of central nervous system development
    • WEN, X., et al. 1998. Large-scale temporal gene expression mapping of central nervous system development. Proc. Natl. Acad. Sci. USA 95: 334-339.
    • (1998) Proc. Natl. Acad. Sci. USA , vol.95 , pp. 334-339
    • Wen, X.1
  • 27
    • 0032441150 scopus 로고    scopus 로고
    • Cluster analysis and display of genome-wide expression patterns
    • EISEN, P.T., et al. 1998. Cluster analysis and display of genome-wide expression patterns. Proc. Natl. Acad. Sci. USA 95: 14863-14868.
    • (1998) Proc. Natl. Acad. Sci. USA , vol.95 , pp. 14863-14868
    • Eisen, P.T.1
  • 28
    • 0033536012 scopus 로고    scopus 로고
    • Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays
    • ALON, U., et al. 1999. 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: 6745-6750.
    • (1999) Proc. Natl. Acad. Sci. USA , vol.96 , pp. 6745-6750
    • Alon, U.1
  • 29
    • 0035221559 scopus 로고    scopus 로고
    • Percolation clustering: A novel approach to the clustering of gene expression patterns in dictyostelium development
    • SASIK, R., et al. 2001. Percolation clustering: a novel approach to the clustering of gene expression patterns in dictyostelium development. PSB Proceedings 6: 335-347.
    • (2001) PSB Proceedings , vol.6 , pp. 335-347
    • Sasik, R.1
  • 30
    • 0034084864 scopus 로고    scopus 로고
    • On the convergence of a clustering algorithm for protein-coding regions in microbial genomes
    • BALDI, P. 2000. On the convergence of a clustering algorithm for protein-coding regions in microbial genomes. Bioinformatics 16: 367-371.
    • (2000) Bioinformatics , vol.16 , pp. 367-371
    • Baldi, P.1
  • 31
    • 0036188158 scopus 로고    scopus 로고
    • Mixture modeling of gene expression data from microarray experiments
    • GHOSH, D. 2001. Mixture modeling of gene expression data from microarray experiments Bioinformatics 18(2): 275-286.
    • (2001) Bioinformatics , vol.18 , Issue.2 , pp. 275-286
    • Ghosh, D.1
  • 32
    • 0036203115 scopus 로고    scopus 로고
    • A mixture model-based approach to the clustering of microarray expression data
    • MCLACHLAN, G.J., R.W. BEAN & D. PEEL. 2002. A mixture model-based approach to the clustering of microarray expression data. Bioinformatics 18: 413-422.
    • (2002) Bioinformatics , vol.18 , pp. 413-422
    • McLachlan, G.J.1    Bean, R.W.2    Peel, D.3
  • 33
    • 0034568109 scopus 로고    scopus 로고
    • "Gene shaving" as a method for identifying distinct sets of genes with similar expression pattern
    • HASTIE, T., et al. 2000. "Gene shaving" as a method for identifying distinct sets of genes with similar expression pattern. Genome Biol. 1: 1-21.
    • (2000) Genome Biol. , vol.1 , pp. 1-21
    • Hastie, T.1
  • 34
    • 0034602774 scopus 로고    scopus 로고
    • Knowledge-based analysis of microarray gene expression data by using support vector machines
    • BROWN, M.P.S., et al. 2000. Knowledge-based analysis of microarray gene expression data by using support vector machines. Proc. Natl. Acad. Sci. USA 97: 262-267.
    • (2000) Proc. Natl. Acad. Sci. USA , vol.97 , pp. 262-267
    • Brown, M.P.S.1
  • 35
    • 0033636139 scopus 로고    scopus 로고
    • Support vector machine classification and validation of cancer tissue samples using microarray expression data
    • FUREY, T.S., 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
  • 36
    • 0027903113 scopus 로고
    • Using Dirichlet mixture priors to derive hidden Markov models for protein families
    • L. Hunter, D. Searls & J. Shavlik, Eds. AAAI/MIT Press, Menlo Park
    • BROWN, M.P., R. HUGHEY, A. KROGH, et al. 1993. Using Dirichlet mixture priors to derive hidden Markov models for protein families. In ISMB-93. L. Hunter, D. Searls & J. Shavlik, Eds.: 47-55. AAAI/MIT Press, Menlo Park.
    • (1993) ISMB-93 , pp. 47-55
    • Brown, M.P.1    Hughey, R.2    Krogh, A.3
  • 37
    • 0027944605 scopus 로고
    • A hidden Markov model that finds genes in E. coli DNA
    • KROGH, A., I.S. MIAN & D. HAUSSLER. 1994. A hidden Markov model that finds genes in E. coli DNA. Nuclei Acids Res. 22: 4768-4778.
    • (1994) Nuclei Acids Res. , vol.22 , pp. 4768-4778
    • Krogh, A.1    Mian, I.S.2    Haussler, D.3
  • 38
    • 0029665409 scopus 로고    scopus 로고
    • Identification of sequence patterns with profile analysis
    • GRIBSKOV, M. & S. VERETNIK. 1996. Identification of sequence patterns with profile analysis. Meth. Enzymol. 266: 198-227.
    • (1996) Meth. Enzymol. , vol.266 , pp. 198-227
    • Gribskov, M.1    Veretnik, S.2
  • 39
    • 0024604438 scopus 로고
    • Methods for calculating the probabilities of finding patterns in sequences
    • STADEN, R. 1989. Methods for calculating the probabilities of finding patterns in sequences. CABIOS 5: 89-96.
    • (1989) CABIOS , vol.5 , pp. 89-96
    • Staden, R.1
  • 40
    • 0029887381 scopus 로고    scopus 로고
    • Hidden Markov models for sequence analysis: Extension and analysis of the basic method
    • R. HUGHEY & A. KROGH. 1996. Hidden Markov models for sequence analysis: extension and analysis of the basic method. CABIOS 12(2): 95-107.
    • (1996) CABIOS , vol.12 , Issue.2 , pp. 95-107
    • Hughey, R.1    Krogh, A.2
  • 41
    • 0030810993 scopus 로고    scopus 로고
    • Finding genes in human DNA with a hidden Markov model
    • HENDERSON, J., S. SALZBERG & K. FASMAN. 1997. Finding genes in human DNA with a hidden Markov model. J. Comput. Biol. 4: 127-141.
    • (1997) J. Comput. Biol. , vol.4 , pp. 127-141
    • Henderson, J.1    Salzberg, S.2    Fasman, K.3
  • 42
    • 0032519353 scopus 로고    scopus 로고
    • GeneMark.hmm: New solutions for gene finding
    • LUKASHIN, A.V. & M. BORODOVSKY. 1998. GeneMark.hmm: new solutions for gene finding. Nuclei Acids Res. 26: 1107-1115.
    • (1998) Nuclei Acids Res. , vol.26 , pp. 1107-1115
    • Lukashin, A.V.1    Borodovsky, M.2
  • 43
    • 0030925920 scopus 로고    scopus 로고
    • Pfam: A comprehensive database of protein domain families based on seed alignment
    • SONNHAMMER, E.L.L., S.R. EDDY & R. DURBY. 1997. Pfam: a comprehensive database of protein domain families based on seed alignment. Protein Structure, Function Genetics 28: 405-420.
    • (1997) Protein Structure, Function Genetics , vol.28 , pp. 405-420
    • Sonnhammer, E.L.L.1    Eddy, S.R.2    Durby, R.3
  • 44
    • 0032438987 scopus 로고    scopus 로고
    • Hidden Markov models for detecting remote protein homologies
    • KARPLUS, K., K.C. BARRETT & R. HUGHEY. 1998. Hidden Markov models for detecting remote protein homologies. Bioinformatics 14: 846-856.
    • (1998) Bioinformatics , vol.14 , pp. 846-856
    • Karplus, K.1    Barrett, K.C.2    Hughey, R.3
  • 45
    • 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.P., et al. 2001. 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): 1131-1142.
    • (2001) Bioinformatics , vol.17 , Issue.12 , pp. 1131-1142
    • Li, L.P.1
  • 47
    • 0029387798 scopus 로고
    • Genetic algorithms, operators, and DNA fragment assembly
    • PARSONS, R.J., S. FORREST & C. BURKS. 1995. Genetic algorithms, operators, and DNA fragment assembly. Machine Learning 21: 11-33.
    • (1995) Machine Learning , vol.21 , pp. 11-33
    • Parsons, R.J.1    Forrest, S.2    Burks, C.3
  • 50
    • 0031461056 scopus 로고    scopus 로고
    • A genetic algorithm for multiple molecular sequence alignment
    • ZHANG, C. & A.K. WONG. 1997. A genetic algorithm for multiple molecular sequence alignment. Comput. Appl. Biosci. 13: 565-581.
    • (1997) Comput. Appl. Biosci. , vol.13 , pp. 565-581
    • Zhang, C.1    Wong, A.K.2
  • 53
    • 51249194645 scopus 로고
    • A logical calculus of the ideas immanent in nervous activity
    • MCCULLOCH, W.S. & W. PITTS. 1943. A logical calculus of the ideas immanent in nervous activity. Bull. Math. Biophys. 5: 115-133.
    • (1943) Bull. Math. Biophys. , vol.5 , pp. 115-133
    • McCulloch, W.S.1    Pitts, W.2
  • 54
    • 0020480251 scopus 로고
    • Use of the perceptron algorithm to distinguish translational initiation in E. coli
    • STORMO, G., T. SCHNEIDER, L. GOLD & A. EHRENFEUCHT. 1982. Use of the perceptron algorithm to distinguish translational initiation in E. coli. Nuclei Acids Res. 10: 2997-3011.
    • (1982) Nuclei Acids Res. , vol.10 , pp. 2997-3011
    • Stormo, G.1    Schneider, T.2    Gold, L.3    Ehrenfeucht, A.4
  • 55
    • 0036183546 scopus 로고    scopus 로고
    • Artificial neural networks distinguish among subtypes of neoplastic colorectal lesions
    • SELARU, F.M., Y. XU, J. YIN, et al. 2002 Artificial neural networks distinguish among subtypes of neoplastic colorectal lesions. Gastroenterology 122: 606-613.
    • (2002) Gastroenterology , vol.122 , pp. 606-613
    • Selaru, F.M.1    Xu, Y.2    Yin, J.3
  • 60
    • 0029933832 scopus 로고    scopus 로고
    • Motif identification neural design for rapid and sensitive protein family search
    • WU, C., S. ZHAO, H.L. CHEN, C.J. LO & J. MCLARTY. 1996. Motif identification neural design for rapid and sensitive protein family search. CABIOS 12(2): 109-118.
    • (1996) CABIOS , vol.12 , Issue.2 , pp. 109-118
    • Wu, C.1    Zhao, S.2    Chen, H.L.3    Lo, C.J.4    McLarty, J.5
  • 61
    • 0028965444 scopus 로고
    • Identification of coding regions in genomic DNA
    • SNYDER, E.E. & G.D. STORMO. 1995. Identification of coding regions in genomic DNA. J. Mol. Biol. 248: 1-18.
    • (1995) J. Mol. Biol. , vol.248 , pp. 1-18
    • Snyder, E.E.1    Stormo, G.D.2
  • 63
    • 0025489075 scopus 로고
    • The self-organizing map
    • KOHONEN, T. 1990. The self-organizing map. Proc. IEEE 78, 1464-1480.
    • (1990) Proc. IEEE , vol.78 , pp. 1464-1480
    • Kohonen, T.1
  • 64
    • 0042863923 scopus 로고    scopus 로고
    • Analysis of gene expression data using self organizing maps
    • TORONEN, P., et al. 1999. Analysis of gene expression data using self organizing maps. FEBS Lett. 451: 142-146.
    • (1999) FEBS Lett. , vol.451 , pp. 142-146
    • Toronen, P.1
  • 65
    • 0033569406 scopus 로고    scopus 로고
    • Molecular classification of cancer: Class discovery and class prediction by gene expression monitoring
    • GOLUB, T.R. 1999. Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 286: 531-537.
    • (1999) Science , vol.286 , pp. 531-537
    • Golub, T.R.1
  • 66
    • 0033027794 scopus 로고    scopus 로고
    • Interpreting patterns of gene expression with self-organizing maps: Methods and application to hematopoietic differtiation
    • TAMAYO, P., et al. 1999. Interpreting patterns of gene expression with self-organizing maps: methods and application to hematopoietic differtiation. Proc. Natl. Acad. Sci. USA 96: 2907-2912.
    • (1999) Proc. Natl. Acad. Sci. USA , vol.96 , pp. 2907-2912
    • Tamayo, P.1
  • 67
    • 0035375137 scopus 로고    scopus 로고
    • Computational analysis of microarray data
    • QUACKENBUSH, J. 2001. Computational analysis of microarray data. Nature Genet. 2: 418-427.
    • (2001) Nature Genet. , vol.2 , pp. 418-427
    • Quackenbush, J.1
  • 69
    • 0035221559 scopus 로고    scopus 로고
    • Percolation clustering: A novel approach to the clustering of gene expression patterns in dictyostelium development
    • SASIK, R., et al. 2001. Percolation clustering: a novel approach to the clustering of gene expression patterns in dictyostelium development. PSB Proc. 6: 335-347.
    • (2001) PSB Proc. , vol.6 , pp. 335-347
    • Sasik, R.1
  • 70
    • 0028748949 scopus 로고
    • Growing cell structures - A self-organizing network for unsupervised and supervised learning
    • FRITZKE, B. 1994. Growing cell structures - a self-organizing network for unsupervised and supervised learning. Neural Network 7: 1141-1160.
    • (1994) Neural Network , vol.7 , pp. 1141-1160
    • Fritzke, B.1
  • 71
    • 0003253640 scopus 로고    scopus 로고
    • Unsupervised neural network for discovery of gene expression patterns in B-cell lymphoma
    • AZUAJE, F. 2001. Unsupervised neural network for discovery of gene expression patterns in B-cell lymphoma. Online J. Bioinform. 1: 26-41.
    • (2001) Online J. Bioinform. , vol.1 , pp. 26-41
    • Azuaje, F.1
  • 72
    • 0031042985 scopus 로고    scopus 로고
    • Phylogenetic reconstruction using an unsupervised growing neural network that adopts the topology of a phylogenetic tree
    • DOPAZO, J. & J.M. CARAZO. 1997. Phylogenetic reconstruction using an unsupervised growing neural network that adopts the topology of a phylogenetic tree. J. Mol. Evolution 44: 226-233.
    • (1997) J. Mol. Evolution , vol.44 , pp. 226-233
    • Dopazo, J.1    Carazo, J.M.2
  • 73
    • 0031595301 scopus 로고    scopus 로고
    • Self-organizing tree growing network for classifying amino acids
    • WANG, H.C., J. DOPAZO & J.M. CARAZO. 1998. Self-organizing tree growing network for classifying amino acids. Bioinformatics 14(4): 376-377.
    • (1998) Bioinformatics , vol.14 , Issue.4 , pp. 376-377
    • Wang, H.C.1    Dopazo, J.2    Carazo, J.M.3
  • 74
    • 0031703681 scopus 로고    scopus 로고
    • Self-organizing tree-growing network for the classification of protein sequences
    • WANG, H.C., J. DOPAZO, L.G. DE LA FRAGA, et al. 1998. Self-organizing tree-growing network for the classification of protein sequences. Protein Sci. 7: 2613-2622.
    • (1998) Protein Sci. , vol.7 , pp. 2613-2622
    • Wang, H.C.1    Dopazo, J.2    De La Fraga, L.G.3
  • 75
    • 0035108235 scopus 로고    scopus 로고
    • A hierarchical unsupervised growing neural network for clustering gene expression patterns
    • HERRERO, J., A. VALENCIA & J. DOPAZO. 2001. A hierarchical unsupervised growing neural network for clustering gene expression patterns. Bioinformatics 17: 126-136.
    • (2001) Bioinformatics , vol.17 , pp. 126-136
    • Herrero, J.1    Valencia, A.2    Dopazo, J.3
  • 76
    • 0017120827 scopus 로고
    • Adaptive pattern classification and universal recoding: I. Parallel development and coding of neural feature detectors
    • GROSSBERG, S. 1976. Adaptive pattern classification and universal recoding: I. Parallel development and coding of neural feature detectors. Biol. Cybernet. 23: 121-134.
    • (1976) Biol. Cybernet. , vol.23 , pp. 121-134
    • Grossberg, S.1
  • 77
    • 0017083148 scopus 로고
    • Adaptive pattern recognition and universal recoding: II. Feedback, expectation, olfaction, and illusions
    • GROSSBERG, S. 1976. Adaptive pattern recognition and universal recoding: II. Feedback, expectation, olfaction, and illusions. Biol. Cybernet. 23: 187-202.
    • (1976) Biol. Cybernet. , vol.23 , pp. 187-202
    • Grossberg, S.1
  • 79
    • 84973857317 scopus 로고
    • Art 2: Selforganisation of stable category recognition codes for analog input patterns
    • CARPENTER, G.A. & S. GROSSBERG. 1987. Art 2: selforganisation of stable category recognition codes for analog input patterns. Appl. Optic. 26: 4919-4930.
    • (1987) Appl. Optic. , vol.26 , pp. 4919-4930
    • Carpenter, G.A.1    Grossberg, S.2
  • 80
    • 0025786440 scopus 로고
    • Art2-a: An adaptive resonance algorithm for rapid category learning and recognition
    • CARPENTER, G.A., S. GROSSBERG & D.B. ROSEN. 1991. Art2-a: an adaptive resonance algorithm for rapid category learning and recognition. Neural Networks 4: 493-504.
    • (1991) Neural Networks , vol.4 , pp. 493-504
    • Carpenter, G.A.1    Grossberg, S.2    Rosen, D.B.3
  • 81
    • 0025235219 scopus 로고
    • Art3: Hierarchical search using chemical transmitters in self-organizing pattern recognition architectures
    • CARPENTER, G.A. & S. GROSSBERG. 1990. Art3: hierarchical search using chemical transmitters in self-organizing pattern recognition architectures. Neural Networks 3: 129-152.
    • (1990) Neural Networks , vol.3 , pp. 129-152
    • Carpenter, G.A.1    Grossberg, S.2
  • 82
    • 0025889915 scopus 로고
    • Artmap: Supervised real-time learning and classification of nonstationary data by a self-organizing neural network
    • CARPENTER, G.A., S. GROSSBERG & J.H. REYNOLDS. 1991. Artmap: supervised real-time learning and classification of nonstationary data by a self-organizing neural network. Neural Networks 4: 565-588.
    • (1991) Neural Networks , vol.4 , pp. 565-588
    • Carpenter, G.A.1    Grossberg, S.2    Reynolds, J.H.3
  • 83
    • 0026408256 scopus 로고
    • Fuzzy art: Fast stable learning and categorization of analog patterns by an adaptive resonance system
    • CARPENTER, G.A., S. GROSSBERG & D.B. ROSEN. 1991. Fuzzy art: fast stable learning and categorization of analog patterns by an adaptive resonance system. Neural Networks 4: 759-771.
    • (1991) Neural Networks , vol.4 , pp. 759-771
    • Carpenter, G.A.1    Grossberg, S.2    Rosen, D.B.3
  • 84
    • 0026923589 scopus 로고
    • Fuzzy artmap: A neural network architecture for incremental supervised learning of analog multidimensional maps
    • CARPENTER, G.A., S. GROSSBERG, N. MARKUZON, et al. 1992. Fuzzy artmap: a neural network architecture for incremental supervised learning of analog multidimensional maps. IEEE Trans. Neural Networks 3(5): 698-713.
    • (1992) IEEE Trans. Neural Networks , vol.3 , Issue.5 , pp. 698-713
    • Carpenter, G.A.1    Grossberg, S.2    Markuzon, N.3
  • 85
    • 0029406205 scopus 로고
    • A fuzzy artmap nonparametric probability estimator for nonstationary pattern recognition problems
    • CARPENTER, G.A., S. GROSSBERG, et al. 1995. A fuzzy artmap nonparametric probability estimator for nonstationary pattern recognition problems. IEEE Trans. Neural Networks 6(6): 1330-1336.
    • (1995) IEEE Trans. Neural Networks , vol.6 , Issue.6 , pp. 1330-1336
    • Carpenter, G.A.1    Grossberg, S.2
  • 86
    • 0002160029 scopus 로고
    • Simplified fuzzy ARTMAP
    • KASUBA, T. 1993. Simplified fuzzy ARTMAP. AI Expert 1825.
    • (1993) AI Expert , pp. 1825
    • Kasuba, T.1
  • 88
    • 0035096538 scopus 로고    scopus 로고
    • A computational neural approach to support the discovery of gene function and classes of cancer
    • AZUAJE, F. 2001. A computational neural approach to support the discovery of gene function and classes of cancer. IEEE Trans. Biomed. Eng. 48: 332-339.
    • (2001) IEEE Trans. Biomed. Eng. , vol.48 , pp. 332-339
    • Azuaje, F.1
  • 89
    • 0028114541 scopus 로고
    • Molecular computation of solutions to combinatorial problems
    • ADLEMAN, L.M. 1994. Molecular computation of solutions to combinatorial problems. Science 266: 1021-1024.
    • (1994) Science , vol.266 , pp. 1021-1024
    • Adleman, L.M.1
  • 90
    • 0036532396 scopus 로고    scopus 로고
    • Gene expression profiling diagnosis through DNA molecular computation
    • MILLS, A.P., JR. 2002. Gene expression profiling diagnosis through DNA molecular computation. Trend. Biotechnol. 20(4): 137-140.
    • (2002) Trend. Biotechnol. , vol.20 , Issue.4 , pp. 137-140
    • Mills A.P., Jr.1
  • 91
    • 0002408054 scopus 로고    scopus 로고
    • DNA analog vector algebra and physical constraints on large-scale DNA-based neural network computation
    • V. E. Winfree & D.K. Gifford, Eds. American Mathematical Society
    • MILLS, A.P., JR., et al. 2000. DNA analog vector algebra and physical constraints on large-scale DNA-based neural network computation. In DNA Based Computers V. E. Winfree & D.K. Gifford, Eds.: 65-73. American Mathematical Society.
    • (2000) DNA Based Computers , pp. 65-73
    • Mills A.P., Jr.1
  • 93
    • 0034960264 scopus 로고    scopus 로고
    • Missing value estimation methods for DNA microarrays
    • TROYANSKAYA, O., et al. 2001. Missing value estimation methods for DNA microarrays. Bioinformatics 17(6): 520-525.
    • (2001) Bioinformatics , vol.17 , Issue.6 , pp. 520-525
    • Troyanskaya, O.1
  • 94
    • 0036191191 scopus 로고    scopus 로고
    • A cluster validity framework for genome expression data
    • AZUAJE, F. 2002. A cluster validity framework for genome expression data. Bioinformatics 18: 319-320.
    • (2002) Bioinformatics , vol.18 , pp. 319-320
    • Azuaje, F.1
  • 95
    • 0037171745 scopus 로고    scopus 로고
    • Microarray technology: An array of opportunities
    • GERSHON, D. 2002. Microarray technology: an array of opportunities. Nature 416: 885-891.
    • (2002) Nature , vol.416 , pp. 885-891
    • Gershon, D.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.