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Updated: Aug 16, 2026

Low Molecular Weight Protein Enrichment on Mesoporous Silica Thin Films for Biomarker Discovery
Published on: April 17, 2012
Analysis of mass spectral serum profiles for biomarker selection
Habtom W Ressom1, Rency S Varghese, Mohamed Abdel-Hamid
1Lombardi Comprehensive Cancer Center, Georgetown University, Washington, DC, USA. hwr@georgetown.edu
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.
Availability:
MATLAB scripts to implement the methods described in this paper are available from the HWR's lab website http://lombardi.georgetown.edu/labpage
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