Analysis of mass spectral serum profiles for biomarker selection

Habtom W. Ressom*, Rency S. Varghese, Mohamed Abdel-Hamid, Sohair Abdel Latif Eissa, Daniel Saha, Lenka Goldman, Emanuel F. Petricoin, Thomas P. Conrads, Timothy D. Veenstra, Christopher A. Loffredo, Radoslav Goldman

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

73 Scopus citations


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.

Original languageEnglish
Pages (from-to)4039-4045
Number of pages7
Issue number21
StatePublished - Nov 2005
Externally publishedYes


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