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Analysis of mass spectral serum profiles for biomarker selection

  1. Author:
    Ressom, H. W.
    Varghese, R. S.
    bdel-Hamid, M.
    Eissa, S. A. L.
    Saha, D.
    Goldman, L.
    Petricoin, E. F.
    Conrads, T. P.
    Veenstra, T. D.
    Loffredo, C. A.
    Goldman, R.

  2. Author Address

    Georgetown Univ, Lombardi Comprehens Canc Ctr, Washington, DC 20057 USA. NHTMRI, Viral Hepatitis Res Lab, Cairo, Egypt. Natl Canc Inst, Cairo, Egypt. NCI, US FDA, Clin Proteom Program, Ctr Biol Evaluat, Bethesda, MD 20892 USA. NCI, SAIC Frederick, Frederick, MD 21701 USA. NCI, Biomed Proteom Program, Frederick, MD 21701 USA Ressom, HW, Georgetown Univ, Lombardi Comprehens Canc Ctr, Washington, DC 20057 USA
    1. Year: 2005
    2. Date: NOV 1
  2. Journal: Bioinformatics
    1. 21
    2. 21
    3. Pages: 4039-4045
  4. Type of Article: Article
  1. Abstract:

    Motivation: Mass spectrometric profiles of peptides and proteins obtained by current technologies are characterized by complex spectra, high dimensionality and substantial noise. These characteristics generate challenges in the discovery of proteins and protein-profiles that distinguish disease states, e.g. cancer patients from healthy individuals. We present low-level methods for the processing of mass spectral data and a machine learning method that combines support vector machines, with particle swarm optimization for biomarker selection.Results: The proposed method identified mass points that achieved high prediction accuracy in distinguishing liver cancer patients from healthy individuals in SELDI-QqTOF profiles of serum

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External Sources

  1. View record in Web of Science®
  2. WOS: 000232901100016

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