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Volumn 23, Issue 15, 2007, Pages 1945-1951

Logistic regression for disease classification using microarray data: Model selection in a large p and small n case

Author keywords

[No Author keywords available]

Indexed keywords

ACUTE LYMPHOBLASTIC LEUKEMIA; ACUTE MYELOBLASTIC LEUKEMIA; ARTICLE; CANCER CLASSIFICATION; DNA MICROARRAY; FEMALE; GENE EXPRESSION; GENETIC MODEL; HUMAN; HUMAN TISSUE; LOGISTIC REGRESSION ANALYSIS; NUCLEOTIDE SEQUENCE; PREDICTION; PRIORITY JOURNAL; SYSTEMATIC ERROR; UTERINE CERVIX CANCER; VALIDATION PROCESS;

EID: 34548125448     PISSN: 13674803     EISSN: 13674811     Source Type: Journal    
DOI: 10.1093/bioinformatics/btm287     Document Type: Article
Times cited : (167)

References (34)
  • 1
    • 0037076322 scopus 로고    scopus 로고
    • Selection bias in gene extraction on the basis of microarry gene-expression data
    • Ambroise,C. and McLachlan,G.J. (2002) Selection bias in gene extraction on the basis of microarry gene-expression data. Proc. Natl Acad. Sci. 99 6562-6566.
    • (2002) Proc. Natl Acad. Sci , vol.99 , pp. 6562-6566
    • Ambroise, C.1    McLachlan, G.J.2
  • 3
    • 4043135554 scopus 로고    scopus 로고
    • Optimal predictive model selection
    • Barbieri,M.M. and Berger,J.O. (2004) Optimal predictive model selection. Ann. Stat., 32, 870-897.
    • (2004) Ann. Stat , vol.32 , pp. 870-897
    • Barbieri, M.M.1    Berger, J.O.2
  • 4
    • 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. R. Stat. Soc., B57, 289-300.
    • (1995) J. R. Stat. Soc , vol.B57 , pp. 289-300
    • Benjamini, Y.1    Hochberg, Y.2
  • 5
    • 1342330535 scopus 로고    scopus 로고
    • (2904) Is cross-validation valid for small sample microarray classification?
    • Braga-Nato,U.M. and Dougherty,E.R. (2904) Is cross-validation valid for small sample microarray classification? Bioinformatics, 20, 374-380
    • Bioinformatics , vol.20 , pp. 374-380
    • Braga-Nato, U.M.1    Dougherty, E.R.2
  • 6
    • 0003010182 scopus 로고
    • Verification of forecasts expressed in terms of probability
    • Brier,G.W. (1950) Verification of forecasts expressed in terms of probability. Mon. Weather Rev., 78, 1-3.
    • (1950) Mon. Weather Rev , vol.78 , pp. 1-3
    • Brier, G.W.1
  • 7
    • 0000521473 scopus 로고
    • Ridge estimators in logistic regression
    • Cessie,S.L. and Van Houwelingen,H.C. (1990) Ridge estimators in logistic regression. Appl. Stat., 41, 191-201.
    • (1990) Appl. Stat , vol.41 , pp. 191-201
    • Cessie, S.L.1    Van Houwelingen, H.C.2
  • 8
    • 0036489046 scopus 로고    scopus 로고
    • Comparison of discrimination methods for the classification of tumors using gene expression data
    • Dudoit,S. et al. (2002) Comparison of discrimination methods for the classification of tumors using gene expression data. J. Am. Stat. Assoc., 97, 77-87.
    • (2002) J. Am. Stat. Assoc , vol.97 , pp. 77-87
    • Dudoit, S.1
  • 9
    • 0033569406 scopus 로고    scopus 로고
    • Molecular classification of cancer: Class discovery and class prediction by gene expression monitoring
    • Golub,T. et al. (1999) Molecular classification of cancer: Class discovery and class prediction by gene expression monitoring. Science 286, 531-536.
    • (1999) Science , vol.286 , pp. 531-536
    • Golub, T.1
  • 10
    • 4944239996 scopus 로고    scopus 로고
    • The estimation of prediction erron covariance penalties and cross-Validation
    • Efron,B. (2004) The estimation of prediction erron covariance penalties and cross-Validation. J. Am. Stat. Associ., 99, 619-632.
    • (2004) J. Am. Stat. Associ , vol.99 , pp. 619-632
    • Efron, B.1
  • 12
    • 0035992248 scopus 로고    scopus 로고
    • Empirical Bayes methods and false discovery rates for microarrays
    • Efron,B. and Tibshirani,R. (2002) Empirical Bayes methods and false discovery rates for microarrays. Genet. Epidemiol., 23, 70-86.
    • (2002) Genet. Epidemiol , vol.23 , pp. 70-86
    • Efron, B.1    Tibshirani, R.2
  • 13
    • 1542784653 scopus 로고    scopus 로고
    • Empirical Bayes analysis of microarray experiment
    • Efron,B. et al. (2001) Empirical Bayes analysis of microarray experiment J. Am. Stati. Associ., 96, 1151-1160.
    • (2001) J. Am. Stati. Associ , vol.96 , pp. 1151-1160
    • Efron, B.1
  • 14
    • 0034863834 scopus 로고    scopus 로고
    • Eilers,P.H. et al. (2001) Classification of microarray data with penalized logistic regression. In Proceedings of SPIE 4266: Progress in Biomedical optics and Imaging, 2, 187-198.
    • Eilers,P.H. et al. (2001) Classification of microarray data with penalized logistic regression. In Proceedings of SPIE Volume 4266: Progress in Biomedical optics and Imaging, Vol 2, 187-198.
  • 15
    • 16344365619 scopus 로고    scopus 로고
    • Classification using partial least squares with penalized logistic regression
    • Fort,G. and Lambert-Lacroix,S. (2005) Classification using partial least squares with penalized logistic regression. Bioinformatics, 21 1104-1111.
    • (2005) Bioinformatics , vol.21 , pp. 1104-1111
    • Fort, G.1    Lambert-Lacroix, S.2
  • 17
    • 0001259111 scopus 로고    scopus 로고
    • Bayesian model Averaging
    • Hoeting,J. et al. (1999) Bayesian model Averaging. Stat. Sci. 14, 382-401.
    • (1999) Stat. Sci , vol.14 , pp. 382-401
    • Hoeting, J.1
  • 18
    • 0020333131 scopus 로고
    • Random-effects models for longitudinal data
    • Laird,N.M. and Ware J.H. (1982) Random-effects models for longitudinal data. Biometrics, 38, 963-974.
    • (1982) Biometrics , vol.38 , pp. 963-974
    • Laird, N.M.1    Ware, J.H.2
  • 19
    • 12244265090 scopus 로고    scopus 로고
    • Gene selection: A Bayesian variable selection approach
    • Lee,K.E. et al. (2003) Gene selection: A Bayesian variable selection approach. Bioinformatics, 19, 90-97.
    • (2003) Bioinformatics , vol.19 , pp. 90-97
    • Lee, K.E.1
  • 20
    • 10444280144 scopus 로고    scopus 로고
    • An extensive comparison of recent classification tools applied to microarray data
    • Lee,J.W. et al. (2005) An extensive comparison of recent classification tools applied to microarray data. Comput. Stat. & Data Anal., 48, 869-885.
    • (2005) Comput. Stat. & Data Anal , vol.48 , pp. 869-885
    • Lee, J.W.1
  • 21
    • 0009435232 scopus 로고    scopus 로고
    • Directional decisions for two-tailed tests: Power, error rates, and sample size
    • Leventhal,L. and Huynh,C. (1996) Directional decisions for two-tailed tests: Power, error rates, and sample size. Psychol. Methods, 1 278-292.
    • (1996) Psychol. Methods , vol.1 , pp. 278-292
    • Leventhal, L.1    Huynh, C.2
  • 22
    • 7244248755 scopus 로고    scopus 로고
    • A comparative study of feature selection and multiclass classification methods for tissue classification based on gene expression
    • Li,T. et al. (2004) A comparative study of feature selection and multiclass classification methods for tissue classification based on gene expression. Bioinformatics, 20, 2429-2437.
    • (2004) Bioinformatics , vol.20 , pp. 2429-2437
    • Li, T.1
  • 23
    • 8844258766 scopus 로고    scopus 로고
    • A mixture model for estimating the local false discovery rate in DNA microarray analysis
    • Liao,J.G. et al. (2004) A mixture model for estimating the local false discovery rate in DNA microarray analysis. Bioinformatics, 20, 2694-2701.
    • (2004) Bioinformatics , vol.20 , pp. 2694-2701
    • Liao, J.G.1
  • 25
    • 0036166439 scopus 로고    scopus 로고
    • Tumor classification by partial least squares using microarray gene expression data
    • Nguyen,D. and Rocke,D. (2002) Tumor classification by partial least squares using microarray gene expression data. Bioinformatics, 18, 39-50.
    • (2002) Bioinformatics , vol.18 , pp. 39-50
    • Nguyen, D.1    Rocke, D.2
  • 26
    • 34548111877 scopus 로고    scopus 로고
    • R Development Core Team R: A Language and Environment for Statistical Computing. R. fondtation for satistical computing. Vienna, AustriaISBN 3-900051-00-3 http://www.R-project.org.
    • R Development Core Team R: A Language and Environment for Statistical Computing. R. fondtation for satistical computing. Vienna, AustriaISBN 3-900051-00-3 http://www.R-project.org.
  • 27
    • 20844431881 scopus 로고    scopus 로고
    • Twilight; a Bioconductor package for estimating the local false discovery rate
    • Scheid,S. and Spang,R. (2005) Twilight; a Bioconductor package for estimating the local false discovery rate. Bioinformatics, 21 2921-2922.
    • (2005) Bioinformatics , vol.21 , pp. 2921-2922
    • Scheid, S.1    Spang, R.2
  • 28
    • 22944456563 scopus 로고    scopus 로고
    • Shen,L. and Tan,E.C. (2005) Dimension reduction-based penalized logistic regression for cancer classification using microarray data. IEEE/ACM Trans. Compu. Biol. Bioinform., 2, 166-175.
    • Shen,L. and Tan,E.C. (2005) Dimension reduction-based penalized logistic regression for cancer classification using microarray data. IEEE/ACM Trans. Compu. Biol. Bioinform., 2, 166-175.
  • 29
    • 0037245343 scopus 로고    scopus 로고
    • Pitfalls in the use of DNA microarray data for diagnostic and prognostic classification
    • Simon,R. et al. (2003) Pitfalls in the use of DNA microarray data for diagnostic and prognostic classification. J. Nat. Cancer Inst, 95, 14-18.
    • (2003) J. Nat. Cancer Inst , vol.95 , pp. 14-18
    • Simon, R.1
  • 30
    • 0035530906 scopus 로고    scopus 로고
    • Shrinkage and penalized likelihood as methods to improve predictive accuracy
    • Van Houwelingen,J.C. (2001) Shrinkage and penalized likelihood as methods to improve predictive accuracy. Statistica Neerlandica, 55, 17-34.
    • (2001) Statistica Neerlandica , vol.55 , pp. 17-34
    • Van Houwelingen, J.C.1
  • 31
    • 0344198131 scopus 로고    scopus 로고
    • Expression genomics of cervical cancer: Molecular classification and prediction of radio-therapy response by DNA microarray
    • Wong,Y.F. et al. (2003) Expression genomics of cervical cancer: molecular classification and prediction of radio-therapy response by DNA microarray. Clini. Cancer Res., 9, 5486-5492.
    • (2003) Clini. Cancer Res , vol.9 , pp. 5486-5492
    • Wong, Y.F.1
  • 32
    • 19544362938 scopus 로고    scopus 로고
    • Bayesian model averaging: Development of an improved multi-calss, gene selection and classification tool for microarray data
    • Yeung,K.Y. et al. (2005) Bayesian model averaging: Development of an improved multi-calss, gene selection and classification tool for microarray data. Bioinformatics, 21, 2394-2402.
    • (2005) Bioinformatics , vol.21 , pp. 2394-2402
    • Yeung, K.Y.1
  • 33
    • 15944363312 scopus 로고    scopus 로고
    • Classification of gene microarrays by penalized logistic regression
    • Zhu,J. and Hastie,T (2004) Classification of gene microarrays by penalized logistic regression. Biostatistics, 5, 427-443.
    • (2004) Biostatistics , vol.5 , pp. 427-443
    • Zhu, J.1    Hastie, T.2
  • 34
    • 4744364173 scopus 로고    scopus 로고
    • Cancer classification and prediction using logistic regression with Bayesian gene selection
    • Zhou,X. et al. (2004) Cancer classification and prediction using logistic regression with Bayesian gene selection. J. Biomed. Inform. 37, 249-259.
    • (2004) J. Biomed. Inform , vol.37 , pp. 249-259
    • Zhou, X.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.