메뉴 건너뛰기




Volumn 45, Issue 5, 2008, Pages 501-520

A review of independent component analysis application to microarray gene expression data

Author keywords

[No Author keywords available]

Indexed keywords

BIOINFORMATICS; CANCER DIAGNOSIS; CANCER GENETICS; CONCEPTUAL FRAMEWORK; DATA ANALYSIS; DATA EXTRACTION; DATA MINING; DATA SYNTHESIS; DNA MICROARRAY; GENE CLUSTER; GENE EXPRESSION PROFILING; GENETIC ALGORITHM; GENETIC ANALYSIS; HUMAN; INDEPENDENT COMPONENT ANALYSIS; INFORMATION TECHNOLOGY; KERNEL METHOD; MACHINE LEARNING; MATHEMATICAL COMPUTING; MATHEMATICAL MODEL; MICROARRAY ANALYSIS; NONHUMAN; REVIEW; YEAST;

EID: 57349130111     PISSN: 07366205     EISSN: None     Source Type: Journal    
DOI: 10.2144/000112950     Document Type: Review
Times cited : (77)

References (66)
  • 1
    • 0034122834 scopus 로고    scopus 로고
    • Independent component approach to the analysis of EEG and MEG recordings
    • Vigário, R., S. Jaakko, and J. Veikko. 2000. Independent component approach to the analysis of EEG and MEG recordings. IEEE Trans. Biomed. Eng. 47:589-593.
    • (2000) IEEE Trans. Biomed. Eng , vol.47 , pp. 589-593
    • Vigário, R.1    Jaakko, S.2    Veikko, J.3
  • 2
    • 18744404816 scopus 로고    scopus 로고
    • Independent component analysis for biomedical signals
    • James, C.J. 2005. Independent component analysis for biomedical signals. Physiol. Meas. 26:R15-R39.
    • (2005) Physiol. Meas , vol.26
    • James, C.J.1
  • 3
    • 21344453001 scopus 로고    scopus 로고
    • Source-density driven independent component analysis approach for FMRI data
    • Hong, B., G.D. Pearlson, and V.D. Calhoun. 2005. Source-density driven independent component analysis approach for FMRI data. Hum. Brain Mapp. 25:297-307.
    • (2005) Hum. Brain Mapp , vol.25 , pp. 297-307
    • Hong, B.1    Pearlson, G.D.2    Calhoun, V.D.3
  • 4
    • 0034122834 scopus 로고    scopus 로고
    • Independent component approach to the analysis of EEG and MEG recordings
    • Vigário, R., S. Jaakko, and J. Veikko. 2000. Independent component approach to the analysis of EEG and MEG recordings. IEEE Trans. Biomed. Eng. 47:589-593.
    • (2000) IEEE Trans. Biomed. Eng , vol.47 , pp. 589-593
    • Vigário, R.1    Jaakko, S.2    Veikko, J.3
  • 7
    • 25444456184 scopus 로고    scopus 로고
    • Blind gene classification - an application of a signal separation method
    • Hori, G., M. Inoue, S.-I. Nishimura, and H. Nakahara. 2001. Blind gene classification - an application of a signal separation method. Genome Informatics 12: 255-256.
    • (2001) Genome Informatics , vol.12 , pp. 255-256
    • Hori, G.1    Inoue, M.2    Nishimura, S.-I.3    Nakahara, H.4
  • 8
    • 0036166753 scopus 로고    scopus 로고
    • Linear modes of gene expression determined by independent component analysis
    • Liebermeister, W. 2002. Linear modes of gene expression determined by independent component analysis. Bioinformatics 18:51-60.
    • (2002) Bioinformatics , vol.18 , pp. 51-60
    • Liebermeister, W.1
  • 10
    • 0028416938 scopus 로고
    • Independent component analysis, a new concept?
    • Comon, P. 1994. Independent component analysis, a new concept? Signal Process. 36:287-314.
    • (1994) Signal Process , vol.36 , pp. 287-314
    • Comon, P.1
  • 11
    • 0029411030 scopus 로고
    • An information-maximization approach to blind separation and blind deconvolution
    • Bell, A.J. and T.J. Sejnowski. 1995. An information-maximization approach to blind separation and blind deconvolution. Neural Comput. 7:1129-1159.
    • (1995) Neural Comput , vol.7 , pp. 1129-1159
    • Bell, A.J.1    Sejnowski, T.J.2
  • 13
    • 0346307721 scopus 로고    scopus 로고
    • A fast fixed-point algorithm for independent component analysis
    • Hyvärinen, A. and E. Oja. 1997. A fast fixed-point algorithm for independent component analysis. Neural Comput. 9:1483-1492.
    • (1997) Neural Comput , vol.9 , pp. 1483-1492
    • Hyvärinen, A.1    Oja, E.2
  • 15
    • 0000466122 scopus 로고    scopus 로고
    • Survey on Independent Component Analysis
    • Hyvärinen, A. 1999. Survey on Independent Component Analysis. Neural Comput. Surv. 2:94-128.
    • (1999) Neural Comput. Surv , vol.2 , pp. 94-128
    • Hyvärinen, A.1
  • 17
    • 1542473171 scopus 로고    scopus 로고
    • Application of independent component analysis to microarrays
    • Lee, S.-L and S. Batzoglou. 2003. Application of independent component analysis to microarrays. Genome Biology 4:R76.
    • (2003) Genome Biology , vol.4
    • Lee, S.-L.1    Batzoglou, S.2
  • 19
    • 12844264782 scopus 로고    scopus 로고
    • Blind source separation and the analysis of microarray data
    • Chiappetta, P., M.C. Roubaud, and B. Torrésani. 2004. Blind source separation and the analysis of microarray data. J. Comput. Biol. 11:1090-1109.
    • (2004) J. Comput. Biol , vol.11 , pp. 1090-1109
    • Chiappetta, P.1    Roubaud, M.C.2    Torrésani, B.3
  • 20
    • 1242341923 scopus 로고    scopus 로고
    • Incipient Alzheimer's disease: Microarray correlation analyses reveal major transcriptional and tumor suppressor responses
    • Blalock, E.M., J.W. Geddes, K.C. Chen, N.M. Porter, W.R. Markesbery, and P.W. Landfield. 2004. Incipient Alzheimer's disease: microarray correlation analyses reveal major transcriptional and tumor suppressor responses. Proc. Natl. Acad. Sci. USA 101:2173-2178.
    • (2004) Proc. Natl. Acad. Sci. USA , vol.101 , pp. 2173-2178
    • Blalock, E.M.1    Geddes, J.W.2    Chen, K.C.3    Porter, N.M.4    Markesbery, W.R.5    Landfield, P.W.6
  • 21
    • 12244298864 scopus 로고    scopus 로고
    • A decomposition model to track gene expression signatures: Preview on observer-independent classification of ovarian cancer
    • Martoglio, A.-M., J.W. Miskin, S.K. Smith, and D.J.C. Mackay. 2002. A decomposition model to track gene expression signatures: preview on observer-independent classification of ovarian cancer. Bioinformatics 18:1617-1624.
    • (2002) Bioinformatics , vol.18 , pp. 1617-1624
    • Martoglio, A.-M.1    Miskin, J.W.2    Smith, S.K.3    Mackay, D.J.C.4
  • 22
    • 0034730140 scopus 로고    scopus 로고
    • Singular value decomposition for genome-wide expression data processing and modeling
    • Alter, O., P.O. Brown, and D. Botstein. 2000. Singular value decomposition for genome-wide expression data processing and modeling. Proc. Nati. Acad. Sci. USA 97:10101-10106.
    • (2000) Proc. Nati. Acad. Sci. USA , vol.97 , pp. 10101-10106
    • Alter, O.1    Brown, P.O.2    Botstein, D.3
  • 26
    • 21444450507 scopus 로고    scopus 로고
    • A variational bayesian mixture modeling framework for cluster analysis of gene-expression data
    • Teschendorff, A.E., Y.Z. Wang, N.L. Barbosa-Morais, J.D. Brenton, and C. Caldas. 2005. A variational bayesian mixture modeling framework for cluster analysis of gene-expression data. Bioinformatics 21:3025-3033.
    • (2005) Bioinformatics , vol.21 , pp. 3025-3033
    • Teschendorff, A.E.1    Wang, Y.Z.2    Barbosa-Morais, N.L.3    Brenton, J.D.4    Caldas, C.5
  • 27
    • 34548444625 scopus 로고    scopus 로고
    • Elucidating the altered transcriptional programs in breast cancer using independent component analysis
    • Teschendorff, A.E., M. Journee, P.A. Absil, R. Sepulchre, and C. Caldas. 2007. Elucidating the altered transcriptional programs in breast cancer using independent component analysis. PLOS Comput. Biol. 3:e161.
    • (2007) PLOS Comput. Biol , vol.3
    • Teschendorff, A.E.1    Journee, M.2    Absil, P.A.3    Sepulchre, R.4    Caldas, C.5
  • 28
    • 0000396062 scopus 로고    scopus 로고
    • Natural gradient works efficiently in learning
    • Amari, S.-I. 1998. Natural gradient works efficiently in learning. Neural Comput. 10:251-276.
    • (1998) Neural Comput , vol.10 , pp. 251-276
    • Amari, S.-I.1
  • 29
    • 0000147501 scopus 로고    scopus 로고
    • Learning the higher-order structure of a nature sound
    • Bell, A.J. and T.J. Sejnowski. 1996. Learning the higher-order structure of a nature sound. Netw: Comput. Neural Syst. 7:261-266.
    • (1996) Netw: Comput. Neural Syst , vol.7 , pp. 261-266
    • Bell, A.J.1    Sejnowski, T.J.2
  • 30
    • 0032612381 scopus 로고    scopus 로고
    • High-order contrasts for independent component analysis
    • Cardoso, J.F. 1999. High-order contrasts for independent component analysis. Neural Comput. 11:157-192.
    • (1999) Neural Comput , vol.11 , pp. 157-192
    • Cardoso, J.F.1
  • 31
    • 0032629347 scopus 로고    scopus 로고
    • Fast and robust fixedpoint algorithms for independent component analysis
    • Hyvärinen, A. 1999. Fast and robust fixedpoint algorithms for independent component analysis. IEEE Trans. Neural Netw. 10:626-634.
    • (1999) IEEE Trans. Neural Netw , vol.10 , pp. 626-634
    • Hyvärinen, A.1
  • 32
    • 0033556834 scopus 로고    scopus 로고
    • Independent component analysis using an extended Infomax algorithm for mixed subgaussian and supergaussian sources
    • Lee, T.W., M. Girolami, and T.J. Sejnowski. 1999. Independent component analysis using an extended Infomax algorithm for mixed subgaussian and supergaussian sources. Neural Comput. 11:417-441.
    • (1999) Neural Comput , vol.11 , pp. 417-441
    • Lee, T.W.1    Girolami, M.2    Sejnowski, T.J.3
  • 37
    • 33747865502 scopus 로고    scopus 로고
    • Independent component analysis-based penalized discriminant method for tumor classification using gene expression data
    • Huang, D.-S. and C.-H. Zheng. 2006. Independent component analysis-based penalized discriminant method for tumor classification using gene expression data. Bioinformatics 22:1855-1862.
    • (2006) Bioinformatics , vol.22 , pp. 1855-1862
    • Huang, D.-S.1    Zheng, C.-H.2
  • 40
    • 4444229045 scopus 로고    scopus 로고
    • Independent component analysis of microarray data in the study of endometrial cancer
    • Saidi, S.A., C.M. Holland, D.P. Kreil, D. MacKay, and D.S. Charnock-Jones. 2004. Independent component analysis of microarray data in the study of endometrial cancer. Oncogene 23:6677-6683.
    • (2004) Oncogene , vol.23 , pp. 6677-6683
    • Saidi, S.A.1    Holland, C.M.2    Kreil, D.P.3    MacKay, D.4    Charnock-Jones, D.S.5
  • 42
    • 30744439853 scopus 로고    scopus 로고
    • Molecular diagnosis of human cancer type by gene expression profiles and independent component analysis
    • Zhang, X.W., Y.L. Yap, D. Wei, F. Chen, and A. Danchin. 2005. Molecular diagnosis of human cancer type by gene expression profiles and independent component analysis. Eur. J Hum. Genet. 13:1303-1311.
    • (2005) Eur. J Hum. Genet , vol.13 , pp. 1303-1311
    • Zhang, X.W.1    Yap, Y.L.2    Wei, D.3    Chen, F.4    Danchin, A.5
  • 43
    • 33748201591 scopus 로고    scopus 로고
    • Independent component analysis reveals new and biologically significant structures in micro array data
    • Frigyesi, A., S. Veerla, D. Lindgren, and M. Hoglund. 2006. Independent component analysis reveals new and biologically significant structures in micro array data. BMC Bioinformatics 7:290-301.
    • (2006) BMC Bioinformatics , vol.7 , pp. 290-301
    • Frigyesi, A.1    Veerla, S.2    Lindgren, D.3    Hoglund, M.4
  • 45
    • 0032612381 scopus 로고    scopus 로고
    • High-order contrasts for independent component analysis
    • Cardoso, J.-F. 1999. High-order contrasts for independent component analysis. Neural Comput. 11:157-192.
    • (1999) Neural Comput , vol.11 , pp. 157-192
    • Cardoso, J.-F.1
  • 47
    • 0011812771 scopus 로고    scopus 로고
    • Kernel independent component analysis
    • Bach, F.R. and M.I. Jordan. 2002. Kernel independent component analysis. J. Mach. Learn. Res. 3:1-48.
    • (2002) J. Mach. Learn. Res , vol.3 , pp. 1-48
    • Bach, F.R.1    Jordan, M.I.2
  • 50
    • 0003994516 scopus 로고    scopus 로고
    • PhD thesis, Department of Physics, University of Cambridge, Cambridge, UK
    • Miskin, J.M. 2002. Ensemble learning for independent component analysis, p. 1-212. PhD thesis, Department of Physics, University of Cambridge, Cambridge, UK.
    • (2002) Ensemble learning for independent component analysis , pp. 1-212
    • Miskin, J.M.1
  • 51
    • 33646184668 scopus 로고    scopus 로고
    • Computational decomposition of molecular signatures based on blind source separation of non-negative dependent sources with NMF
    • Zhang, J.Y., L. Wei, and Y. Wang. 2003. Computational decomposition of molecular signatures based on blind source separation of non-negative dependent sources with NMF. 2003 IEEE 13th Workshop on Neural Networks for Signal Processing. 409-418.
    • (2003) 2003 IEEE 13th Workshop on Neural Networks for Signal Processing , pp. 409-418
    • Zhang, J.Y.1    Wei, L.2    Wang, Y.3
  • 53
    • 36348966695 scopus 로고    scopus 로고
    • On the convergence of multiplicative update algorithms for nonnegative matrix factorization
    • Lin, C.-J. 2007. On the convergence of multiplicative update algorithms for nonnegative matrix factorization. IEEE Trans. Neural Netw. 18:1589-1596.
    • (2007) IEEE Trans. Neural Netw , vol.18 , pp. 1589-1596
    • Lin, C.-J.1
  • 54
    • 84898964201 scopus 로고    scopus 로고
    • Algorithms for non-negative matrix factorization
    • MIT Press, Cambridge, MA
    • Lee, D.D. and H.S. Seung. 2001. Algorithms for non-negative matrix factorization. In Advances in Neural Information Processing Systems. 13:556-562. MIT Press, Cambridge, MA.
    • (2001) Advances in Neural Information Processing Systems , vol.13 , pp. 556-562
    • Lee, D.D.1    Seung, H.S.2
  • 55
    • 12844264782 scopus 로고    scopus 로고
    • Blind source separation and the analysis of microarray data
    • Chiappetta, P., M.C. Roubaud, and B. Torrésani. 2004. Blind source separation and the analysis of microarray data. J. Comput. Biol. 11:1090-1109.
    • (2004) J. Comput. Biol , vol.11 , pp. 1090-1109
    • Chiappetta, P.1    Roubaud, M.C.2    Torrésani, B.3
  • 56
    • 3042594840 scopus 로고    scopus 로고
    • Validating the independent components of neuroimaging time-series via clustering and visualization
    • Himberg, J., A. Hyvärinen, and F. Esposito. 2004. Validating the independent components of neuroimaging time-series via clustering and visualization. Neuroimage 22:1214-1222.
    • (2004) Neuroimage , vol.22 , pp. 1214-1222
    • Himberg, J.1    Hyvärinen, A.2    Esposito, F.3
  • 57
    • 30344445232 scopus 로고    scopus 로고
    • An extended speech denoising method using GGM-based ICA feature extraction
    • Kong, W., Y. Zhou, and J. Yang. 2004. An extended speech denoising method using GGM-based ICA feature extraction. Lecture Notes Comput. Sci. 3287:296-302.
    • (2004) Lecture Notes Comput. Sci , vol.3287 , pp. 296-302
    • Kong, W.1    Zhou, Y.2    Yang, J.3
  • 58
    • 33745330441 scopus 로고    scopus 로고
    • Efficient feature extraction and de-noising method for Chinese speech signals using GGM-based ICA
    • Yang, B. and W. Kong. 2005. Efficient feature extraction and de-noising method for Chinese speech signals using GGM-based ICA. Lecture Notes Comput. Sci. 3773:925-932.
    • (2005) Lecture Notes Comput. Sci , vol.3773 , pp. 925-932
    • Yang, B.1    Kong, W.2
  • 59
    • 33745888687 scopus 로고    scopus 로고
    • Higher-order feature extraction of non-gaussian acoustic signals using GGM-based ICA
    • Kong, W. and B. Yang. 2006. Higher-order feature extraction of non-gaussian acoustic signals using GGM-based ICA. Lecture Notes Comput. Sci. 3972:712-718.
    • (2006) Lecture Notes Comput. Sci , vol.3972 , pp. 712-718
    • Kong, W.1    Yang, B.2
  • 60
    • 0032549745 scopus 로고    scopus 로고
    • Genomic cis-regulatory logic: Experimental and computational analysis of a sea urchin gene
    • Yuh, C.H., H. Bolouri, and E.H. Davidson. 1998. Genomic cis-regulatory logic: experimental and computational analysis of a sea urchin gene. Science 279:1896-1902.
    • (1998) Science , vol.279 , pp. 1896-1902
    • Yuh, C.H.1    Bolouri, H.2    Davidson, E.H.3
  • 61
    • 0038241770 scopus 로고    scopus 로고
    • Development of genetic circuitry exhibiting toggle switch or oscillatory behavior in Escherichia coli
    • Atkinson, M.R., M.A. Savageau, J.T. Myers, and A.J. Ninfa. 2003. Development of genetic circuitry exhibiting toggle switch or oscillatory behavior in Escherichia coli. Cell 113:597-607.
    • (2003) Cell , vol.113 , pp. 597-607
    • Atkinson, M.R.1    Savageau, M.A.2    Myers, J.T.3    Ninfa, A.J.4
  • 62
    • 0035578429 scopus 로고    scopus 로고
    • Design principles for elementary gene circuits: Elements, methods, and examples
    • Savageau, M.A. 2001. Design principles for elementary gene circuits: elements, methods, and examples. Chaos 11:142-159.
    • (2001) Chaos , vol.11 , pp. 142-159
    • Savageau, M.A.1
  • 64
    • 34548125749 scopus 로고    scopus 로고
    • Kernel ICA feature extraction for spectral recognition of celestial objects
    • Man and Cybernetics
    • Bai, L., A. Xu, P. Guo, and Y. Jia. 2006. Kernel ICA feature extraction for spectral recognition of celestial objects. IEEE International Conference on Systems, Man and Cybernetics, 2006. 5:3922-3926.
    • (2006) IEEE International Conference on Systems , vol.5 , pp. 3922-3926
    • Bai, L.1    Xu, A.2    Guo, P.3    Jia, Y.4


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.