메뉴 건너뛰기




Volumn 9780387475097, Issue , 2007, Pages 149-172

Feature selection and dimensionality reduction in genomics and proteomics

Author keywords

[No Author keywords available]

Indexed keywords

GENES; MASS SPECTROMETRY; PROTEOMICS; SENSOR NETWORKS;

EID: 36448949883     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.1007/978-0-387-47509-7_7     Document Type: Chapter
Times cited : (24)

References (50)
  • 2
    • 0034948896 scopus 로고    scopus 로고
    • A bayesian framework for the analysis of microarray expression data: Regularized t-test and statistical inferences of gene changes
    • Baldi, P. and Long, A. D. (2001). A bayesian framework for the analysis of microarray expression data: regularized t-test and statistical inferences of gene changes. Bioinformatics, 17:509-519.
    • (2001) Bioinformatics , vol.17 , pp. 509-519
    • Baldi, P.1    Long, A.D.2
  • 3
    • 0014060964 scopus 로고
    • A clustering technique for summarizing multivariate data
    • Ball, G. and Hall, D. (1967). A clustering technique for summarizing multivariate data. Behav. Science, 12:153-155.
    • (1967) Behav. Science , vol.12 , pp. 153-155
    • Ball, G.1    Hall, D.2
  • 4
    • 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. Comp. Biol, 7:559-584.
    • (2000) J. Comp. Biol , vol.7 , pp. 559-584
    • Ben-Dor, A.1    Bruhn, L.2    Friedman, N.3
  • 5
    • 0001677717 scopus 로고
    • Controlling the false discovery rate: A practical and powerful approach to multiple testing
    • Benjamini, Y. and Hochberg, Y. (1995). Controlling the false discovery rate: A practical and powerful approach to multiple testing. J. Roy. Stat. Soc. B, 57:289-300.
    • (1995) J. Roy. Stat. Soc. B , vol.57 , pp. 289-300
    • Benjamini, Y.1    Hochberg, Y.2
  • 6
    • 0031334221 scopus 로고    scopus 로고
    • Selection of relevant features and examples in machine learning
    • Blum, A. and Langley, P. (1997). Selection of relevant features and examples in machine learning. Art. Intell., 97(1-2):245-271.
    • (1997) Art. Intell. , vol.97 , Issue.1-2 , pp. 245-271
    • Blum, A.1    Langley, P.2
  • 8
    • 27144489164 scopus 로고    scopus 로고
    • A tutorial on support vector machines for pattern recognition
    • Burges, C. J. C. (1998). A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery, 2(2):121-167.
    • (1998) Data Mining and Knowledge Discovery , vol.2 , Issue.2 , pp. 121-167
    • Burges, C.J.C.1
  • 9
    • 0000917558 scopus 로고
    • The use of maximum likelihood estimates in chi2 tests for goodness-of-fit
    • Chernoff, H. and Lehmann, EX. (1954). The use of maximum likelihood estimates in chi2 tests for goodness-of-fit. The Annals of Mathematical Statistics, 25:576-586.
    • (1954) The Annals of Mathematical Statistics , vol.25 , pp. 576-586
    • Chernoff, H.1    Lehmann, E.X.2
  • 10
    • 0000490505 scopus 로고
    • A review of classification
    • Cormack, R. M. (1971). A review of classification. J. Roy. Stat. Soc. A, 134:321-367.
    • (1971) J. Roy. Stat. Soc. A , vol.134 , pp. 321-367
    • Cormack, R.M.1
  • 12
    • 0002629270 scopus 로고
    • Maximum likelihood from incomplete data via the EM algorithm
    • Dempster, A. P., Laird, N. M., and Rubin, D. B. (1977). Maximum likelihood from incomplete data via the EM algorithm. J. Roy. Stat. Soc. B, 34:1-38.
    • (1977) J. Roy. Stat. Soc. B , vol.34 , pp. 1-38
    • Dempster, A.P.1    Laird, N.M.2    Rubin, D.B.3
  • 14
    • 0001788160 scopus 로고
    • Some comments on maximum likelihood and partial least squares methods
    • Dijkstra, T. (1983). Some comments on maximum likelihood and partial least squares methods. J. Econometrics, 22:67-90.
    • (1983) J. Econometrics , vol.22 , pp. 67-90
    • Dijkstra, T.1
  • 16
    • 0032441150 scopus 로고    scopus 로고
    • Cluster analysis and display of genome-wide expression patterns
    • Eisen, M. B., Spellman, P. T., Brown, P. O., and Botstein, D. (1998). Cluster analysis and display of genome-wide expression patterns. Proc. Natl. Acad. Sci. USA, 95(25):14863-14868.
    • (1998) Proc. Natl. Acad. Sci. USA , vol.95 , Issue.25 , pp. 14863-14868
    • Eisen, M.B.1    Spellman, P.T.2    Brown, P.O.3    Botstein, D.4
  • 17
    • 0033636139 scopus 로고    scopus 로고
    • Support vector machine classification and validation of cancer tissue samples using microarray expression data
    • Furey, T. S., Christianini, N., and Duffy, N., et al. (2000). Support vector machine classification and validation of cancer tissue samples using microarray expression data. Bioinformatics, 16(10):906-914.
    • (2000) Bioinformatics , vol.16 , Issue.10 , pp. 906-914
    • Furey, T.S.1    Christianini, N.2    Duffy, N.3
  • 18
    • 0033569406 scopus 로고    scopus 로고
    • Molecular classification of cancer: Class discovery and class prediction by gene expression monitoring
    • Golub, T. R., Slonim, D. K., and Tamayo, P., et al. (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    Slonim, D.K.2    Tamayo, P.3
  • 20
    • 0345399126 scopus 로고
    • The probable error of a mean
    • Gosser, W. S. (1908). The probable error of a mean. BIOMETRIKA, 6:1-25.
    • (1908) Biometrika , vol.6 , pp. 1-25
    • Gosser, W.S.1
  • 21
    • 33745561205 scopus 로고    scopus 로고
    • An introduction to variable and feature selection
    • Guyon, I. and Elisseeff, A. (2003). An introduction to variable and feature selection. J. Machine Learning Res., 3:1157-1182.
    • (2003) J. Machine Learning Res. , vol.3 , pp. 1157-1182
    • Guyon, I.1    Elisseeff, A.2
  • 22
    • 0020083498 scopus 로고
    • The meaning and use of the area under a receiver operating characteristic (ROC) curve
    • Hanley, J. A. and McNeil, B. J. (1982). The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology, 143(1):29-36.
    • (1982) Radiology , vol.143 , Issue.1 , pp. 29-36
    • Hanley, J.A.1    McNeil, B.J.2
  • 24
    • 28444459515 scopus 로고    scopus 로고
    • Feature selection for classification of SELDI-TOF MS proteomic profiles
    • Hauskrecht, M., Pelikan, R., and Malehorn, D. E., et al. (2005). Feature selection for classification of SELDI-TOF MS proteomic profiles. Appl. Bioinf., 4(4):227-246.
    • (2005) Appl. Bioinf. , vol.4 , Issue.4 , pp. 227-246
    • Hauskrecht, M.1    Pelikan, R.2    Malehorn, D.E.3
  • 26
    • 0026191274 scopus 로고
    • Blind separation of sources, part 1: An adaptive algorithm based on neuromimetic architecture
    • Jutten, C. and Herault, J. (1991). Blind separation of sources, part 1: An adaptive algorithm based on neuromimetic architecture. Signal Process., 24(1):1-10.
    • (1991) Signal Process. , vol.24 , Issue.1 , pp. 1-10
    • Jutten, C.1    Herault, J.2
  • 27
    • 84872481598 scopus 로고
    • The treatment of ties in ranking problems
    • Kendall, M. G. (1945). The treatment of ties in ranking problems. Biometrika, 33(3):239-251.
    • (1945) Biometrika , vol.33 , Issue.3 , pp. 239-251
    • Kendall, M.G.1
  • 28
    • 26444479778 scopus 로고
    • Optimization by simulated annealing
    • Kirkpatrick, S., Gelatt, C, and Vecchi, M. (1983). Optimization by simulated annealing. Science, 220:671-680.
    • (1983) Science , vol.220 , pp. 671-680
    • Kirkpatrick, S.1    Gelatt, C.2    Vecchi, M.3
  • 29
    • 0002959696 scopus 로고    scopus 로고
    • The wrapper approach
    • Liu, H. and Motoda, H., editors, Kluwer Academic Publishers, Norwell, MA, USA
    • Kohavi, R. and John, G. (1998). The wrapper approach. In Liu, H. and Motoda, H., editors, Feature Selection for Knowledge Discovery and Data Mining, pages 33-50. Kluwer Academic Publishers, Norwell, MA, USA.
    • (1998) Feature Selection for Knowledge Discovery and Data Mining , pp. 33-50
    • Kohavi, R.1    John, G.2
  • 31
    • 84973821474 scopus 로고
    • Computer-assisted multicrossvalidation in regression analysis
    • Krus, D. J. and Fuller, E. A. (1982). Computer-assisted multicrossvalidation in regression analysis. Educational and Psychological Measurement, 42:187-193.
    • (1982) Educational and Psychological Measurement , vol.42 , pp. 187-193
    • Krus, D.J.1    Fuller, E.A.2
  • 35
    • 0001457509 scopus 로고
    • Some methods for classification and analysis of multivariate observations
    • McQueen, J. (1967). Some methods for classification and analysis of multivariate observations. Proc. 5th Berkeley Symp. Math. Stat. Prob., pages 281-297.
    • (1967) Proc. 5th Berkeley Symp. Math. Stat. Prob. , pp. 281-297
    • McQueen, J.1
  • 36
    • 0001854616 scopus 로고    scopus 로고
    • Assessing relevance determination methods using DELVE
    • Bishop, CM., editor, Springer
    • Neal, R. (1998). Assessing relevance determination methods using DELVE. In Bishop, CM., editor, Neural Networks and Machine Learning, pages 28-32. Springer.
    • (1998) Neural Networks and Machine Learning , pp. 28-32
    • Neal, R.1
  • 37
    • 13244270483 scopus 로고    scopus 로고
    • Cageda: A web application for the integrated analysis of global gene expression patterns in cancer
    • Patel, S. and Lyons-Weiler, J. (2004). caGEDA: A web application for the integrated analysis of global gene expression patterns in cancer. Appl. Bioinf., 3(1):49-62.
    • (2004) Appl. Bioinf. , vol.3 , Issue.1 , pp. 49-62
    • Patel, S.1    Lyons-Weiler, J.2
  • 39
    • 0034050902 scopus 로고    scopus 로고
    • Systematic variation in gene expression patterns in human cancer cell lines
    • Ross, D. T., Scherf, U., and Eisen, M. B., et al. (2000). Systematic variation in gene expression patterns in human cancer cell lines. Nat. Gen., 24:227-235.
    • (2000) Nat. Gen. , vol.24 , pp. 227-235
    • Ross, D.T.1    Scherf, U.2    Eisen, M.B.3
  • 43
    • 33646173259 scopus 로고    scopus 로고
    • Spectral clustering gene ontology terms to group genes by function
    • Speer, N., Spieth, C, and Zell, A. (2005). Spectral clustering gene ontology terms to group genes by function. Lecture Notes in Bioinformatics, 3692:001-012.
    • (2005) Lecture Notes in Bioinformatics , vol.3692 , pp. 001-012
    • Speer, N.1    Spieth, C.2    Zell, A.3
  • 44
    • 0041423883 scopus 로고    scopus 로고
    • SAM thresholding and false discovery rates for detecting differential gene expression in DNA microarrays
    • Parmigiani, G., Garrett, E. S., Irizarry, R. A., and Zeger, S. L., editors, Springer, New York
    • Storey, J. D. and Tibshirani, R. (2003). SAM thresholding and false discovery rates for detecting differential gene expression in DNA microarrays. In Parmigiani, G., Garrett, E. S., Irizarry, R. A., and Zeger, S. L., editors, The Analysis of Gene Expression Data: Methods and Software, pages 272-290. Springer, New York.
    • (2003) The Analysis of Gene Expression Data: Methods and Software , pp. 272-290
    • Storey, J.D.1    Tibshirani, R.2
  • 45
    • 0035942271 scopus 로고    scopus 로고
    • Significance analysis of microarrays applied to the ionizing radiation response
    • Tusher, V. G., Tibshirani, R., and Chu, G. (2001). Significance analysis of microarrays applied to the ionizing radiation response. Proc. Natl Acad. Sci. USA, 98(9):5116-5121.
    • (2001) Proc. Natl Acad. Sci. USA , vol.98 , Issue.9 , pp. 5116-5121
    • Tusher, V.G.1    Tibshirani, R.2    Chu, G.3
  • 46
    • 0015588092 scopus 로고
    • The mutual information principle and applications
    • Tzannes, N. S. and Noonan, J. P. (1973). The mutual information principle and applications. Information and Control, 22(1):1-12.
    • (1973) Information and Control , vol.22 , Issue.1 , pp. 1-12
    • Tzannes, N.S.1    Noonan, J.P.2
  • 49
    • 0001884644 scopus 로고
    • Individual comparisons by ranking methods
    • Wilcoxon, F. (1945). Individual comparisons by ranking methods. Biometrics Bulletin, 1:80-83.
    • (1945) Biometrics Bulletin , vol.1 , pp. 80-83
    • Wilcoxon, F.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.