• DocumentCode
    827125
  • Title

    Quantitative analysis of proteomics using data mining

  • Author

    Yen, Chia-Yu ; Helmike, S.M. ; Cios, Krzysztof J. ; Perryman, M. Benjamin ; Duncan, Mark W.

  • Author_Institution
    Colorado Univ., Denver, CO, USA
  • Volume
    24
  • Issue
    3
  • fYear
    2005
  • Firstpage
    67
  • Lastpage
    72
  • Abstract
    The paper aims to develop an automated system that would ensure a robust peptide quantification process, which would permit researchers to quantify desired proteins faster and with greater reliability. Because of the uniqueness of the data used, biochemists´ expertise and data mining methods were employed in this work. The system includes two main system components: one for the discovery of two quantification peptides and the internal standard peptide and the other for protein quantification in patient samples. If the required input data are available, each subsystem can be run separately. The developed system can be applied to similar problems because our design is flexible, allowing for easy adaptation.
  • Keywords
    biochemistry; data mining; medical diagnostic computing; molecular biophysics; patient diagnosis; proteins; automated system; biochemistry; data mining; internal standard peptide; patient samples; protein quantification; proteomics; robust peptide quantification process; Data mining; Heart; Humans; Mass spectroscopy; Measurement standards; Peptides; Protein engineering; Proteomics; Signal design; Signal processing; Algorithms; Databases, Protein; Gene Expression Profiling; Humans; Information Storage and Retrieval; Myocardium; Myosin Heavy Chains; Proteomics; Sequence Alignment; Sequence Analysis, Protein; Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization;
  • fLanguage
    English
  • Journal_Title
    Engineering in Medicine and Biology Magazine, IEEE
  • Publisher
    ieee
  • ISSN
    0739-5175
  • Type

    jour

  • DOI
    10.1109/MEMB.2005.1436462
  • Filename
    1436462