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Volumn 54, Issue 3, 2013, Pages 165-168

Sequential projection pursuit principal component analysis - Dealing with missing data associated with new -omics technologies

Author keywords

Censored data; Missing data; Optimization; Principal component analysis; Sequential projection pursuit

Indexed keywords

ALGORITHM; ANALYSIS OF VARIANCE; ARTICLE; LIMIT OF DETECTION; LIQUID CHROMATOGRAPHY; MASS SPECTROMETRY; MEASUREMENT ACCURACY; PRINCIPAL COMPONENT ANALYSIS; PROTEOMICS; SIMULATION;

EID: 84876495055     PISSN: 07366205     EISSN: 19409818     Source Type: Journal    
DOI: 10.2144/000113978     Document Type: Article
Times cited : (12)

References (15)
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    • Combined statistical analyses of peptide intensities and peptide occurrences improves identification of significant peptides from MS-based proteomics data
    • Webb-Robertson, B.J., L.A. McCue, K.M. Waters, M.M. Matzke, J.M. Jacobs, T.O. Metz, S.M. Varnum, and J.G. Pounds. 2010. Combined statistical analyses of peptide intensities and peptide occurrences improves identification of significant peptides from MS-based proteomics data. J. Proteome Res. 9:5748-5756.
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    • Webb-Robertson, B.J.1    McCue, L.A.2    Waters, K.M.3    Matzke, M.M.4    Jacobs, J.M.5    Metz, T.O.6    Varnum, S.M.7    Pounds, J.G.8
  • 8
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    • A projection pursuit algorithm for exploratory data analysis
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    • Sequential projection pursuit using genetic algorithms for data mining of analytical data
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* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.