메뉴 건너뛰기




Volumn 25, Issue 8, 2009, Pages 1076-1077

Supervised feature selection in mass spectrometry-based proteomic profiling by blockwise boosting

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHM; ANALYTICAL ERROR; ARTICLE; BLOCKWISE BOOSTING; CLASSIFIER; COMPUTER PREDICTION; GENE EXPRESSION; MACHINE LEARNING; MASS SPECTROMETRY; MATHEMATICAL ANALYSIS; MICROARRAY ANALYSIS; PRIORITY JOURNAL; PROTEOMICS; VALIDATION PROCESS;

EID: 64549129955     PISSN: 13674803     EISSN: 14602059     Source Type: Journal    
DOI: 10.1093/bioinformatics/btp094     Document Type: Article
Times cited : (9)

References (13)
  • 1
    • 42049118310 scopus 로고    scopus 로고
    • Machine learning methods for predictive proteomics
    • Barla,A. et al. (2008) Machine learning methods for predictive proteomics. Brief. Bioinform., 9, 119-128.
    • (2008) Brief. Bioinform , vol.9 , pp. 119-128
    • Barla, A.1
  • 2
    • 0038391397 scopus 로고    scopus 로고
    • Boosting for tumor classification with gene expression data
    • Dettling,M. and Bühlmann,P. (2003) Boosting for tumor classification with gene expression data. Bioinformatics, 19, 1061-1069.
    • (2003) Bioinformatics , vol.19 , pp. 1061-1069
    • Dettling, M.1    Bühlmann, P.2
  • 4
    • 0034164230 scopus 로고    scopus 로고
    • Additive logistic regression: A statistical view of boosting
    • Friedman,J.H. et al. (2000) Additive logistic regression: A statistical view of boosting. Ann. Stat., 28, 337-407.
    • (2000) Ann. Stat , vol.28 , pp. 337-407
    • Friedman, J.H.1
  • 5
    • 41649110725 scopus 로고    scopus 로고
    • Autocorrelated logistic ridge regression for prediction based on proteomics spectra
    • Article
    • Goeman,J.J. (2008) Autocorrelated logistic ridge regression for prediction based on proteomics spectra. Stat. Appl. Genet. Mol. Biol. 7, Article 10.
    • (2008) Stat. Appl. Genet. Mol. Biol , vol.7 , pp. 10
    • Goeman, J.J.1
  • 6
    • 64549153414 scopus 로고    scopus 로고
    • Guo,Y. et al, 2005 rda: Shrunken Centroids Regularized Discriminant Analysis. R package version 1.0
    • Guo,Y. et al. (2005) rda: Shrunken Centroids Regularized Discriminant Analysis. R package version 1.0.
  • 7
    • 33845413755 scopus 로고    scopus 로고
    • Regularized linear discriminant analysis and its application in microarrays
    • Guo,Y. et al. (2007) Regularized linear discriminant analysis and its application in microarrays. Biostatistics, 8, 86-100.
    • (2007) Biostatistics , vol.8 , pp. 86-100
    • Guo, Y.1
  • 8
    • 41649121368 scopus 로고    scopus 로고
    • A classification model for the Leiden proteomics competition
    • Article
    • Hoefsloot,H.C.J. et al. (2008). A classification model for the Leiden proteomics competition. Stat. Appl. Genet. Mol. Biol., 7 Article 8.
    • (2008) Stat. Appl. Genet. Mol. Biol , vol.7 , pp. 8
    • Hoefsloot, H.C.J.1
  • 9
    • 0037120949 scopus 로고    scopus 로고
    • Serum proteomic patterns for detection of prostate cancer
    • Petricoin,E.F. et al. (2002) Serum proteomic patterns for detection of prostate cancer. J. Natl Cancer Inst., 94, 1576-1578.
    • (2002) J. Natl Cancer Inst , vol.94 , pp. 1576-1578
    • Petricoin, E.F.1
  • 10
    • 35748932852 scopus 로고    scopus 로고
    • R Development Core Team , R Foundation for Statistical Computing, Vienna, Austria, ISBN 3-900051-07-0
    • R Development Core Team (2007) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, ISBN 3-900051-07-0.
    • (2007) R: A Language and Environment for Statistical Computing
  • 11
    • 10244279272 scopus 로고    scopus 로고
    • Sample classification from protein mass spectrometry, by 'peak probability contrasts'
    • Tibshirani,R. et al. (2004) Sample classification from protein mass spectrometry, by 'peak probability contrasts'. Bioinformatics 20, 3034-3044.
    • (2004) Bioinformatics , vol.20 , pp. 3034-3044
    • Tibshirani, R.1
  • 12
    • 64549144450 scopus 로고    scopus 로고
    • Feature extraction in signal regression: A boosting technique for functional data regression
    • accepted for publication
    • Tutz,G. and Gertheiss,J. (2009) Feature extraction in signal regression: a boosting technique for functional data regression. J. Comput. Graphical Stat., (accepted for publication).
    • (2009) J. Comput. Graphical Stat
    • Tutz, G.1    Gertheiss, J.2
  • 13
    • 0142008803 scopus 로고    scopus 로고
    • Adata-analytic strategy for protein biomarker discovery: Profiling of high-dimensional proteomic data for cancer detection
    • Yasui,Y. et al. (2003) Adata-analytic strategy for protein biomarker discovery: Profiling of high-dimensional proteomic data for cancer detection. Biostatistics, 4, 449-463.
    • (2003) Biostatistics , vol.4 , pp. 449-463
    • Yasui, Y.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.