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Christian G. Huber: [Publications] [Author Rank by year] [Co-authors] [Prefers] [Cites] [Cited by]

Publications of Author

  1. Clemens Gröpl, Eva Lange, Knut Reinert, Oliver Kohlbacher, Marc Sturm, Christian G. Huber, Bettina M. Mayr, Christoph L. Klein
    Algorithms for the Automated Absolute Quantification of Diagnostic Markers in Complex Proteomics Samples. [Citation Graph (0, 0)][DBLP]
    CompLife, 2005, pp:151-162 [Conf]
  2. Nathanaël Delmotte, Bettina M. Mayr, Andreas Leinenbach, Knut Reinert, Oliver Kohlbacher, Christoph L. Klein, Christian G. Huber
    Evaluation of LC-MS data for the absolute quantitative analysis of marker proteins. [Citation Graph (0, 0)][DBLP]
    Computational Proteomics, 2005, pp:- [Conf]
  3. Christian G. Huber, Oliver Kohlbacher, Knut Reinert
    05471 Executive Summary - Computational Proteomics. [Citation Graph (0, 0)][DBLP]
    Computational Proteomics, 2005, pp:- [Conf]
  4. Christian G. Huber, Oliver Kohlbacher, Knut Reinert
    05471 Abstract Collection - Computational Proteomics. [Citation Graph (0, 0)][DBLP]
    Computational Proteomics, 2005, pp:- [Conf]
  5. Christian Schley, Matthias Altmeyer, Rolf Müller, Christian G. Huber
    Multidimensional Peptide/Protein Analysis and Identification by Sequence Database Search Using Mass Spectrometric Data. [Citation Graph (0, 0)][DBLP]
    Computational Proteomics, 2005, pp:- [Conf]
  6. Marc Sturm, Sascha Quinten, Christian G. Huber, Oliver Kohlbacher
    A machine learning approach for prediction of DNA and peptide retention times. [Citation Graph (0, 0)][DBLP]
    Computational Proteomics, 2005, pp:- [Conf]
  7. Hansjörg Toll, Peter Berger, Andreas Hofmann, Andreas Hildebrandt, Herbert Oberacher, Hans-Peter Lenhof, Christian G. Huber
    Glycosylation Patterns of Proteins Studied by Liquid Chromatography-Mass Spectrometry and Bioinformatic Tools. [Citation Graph (0, 0)][DBLP]
    Computational Proteomics, 2005, pp:- [Conf]

  8. A geometric approach for the alignment of liquid chromatography - mass spectrometry data. [Citation Graph (, )][DBLP]


  9. Statistical learning of peptide retention behavior in chromatographic separations: a new kernel-based approach for computational proteomics. [Citation Graph (, )][DBLP]


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