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Bioinformatics Advance Access originally published online on September 13, 2005
Bioinformatics 2005 21(21):4039-4045; doi:10.1093/bioinformatics/bti670
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© The Author 2005. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions{at}oxfordjournals.org

Analysis of mass spectral serum profiles for biomarker selection

Habtom W. Ressom 1,*, Rency S. Varghese 1, Mohamed Abdel-Hamid 2, Sohair Abdel-Latif Eissa 3, Daniel Saha 1, Lenka Goldman 1, Emanuel F. Petricoin 4, Thomas P. Conrads 5, Timothy D. Veenstra 5, Christopher A. Loffredo 1 and Radoslav Goldman 1

1Lombardi Comprehensive Cancer Center, Georgetown University Washington, DC, USA
2Viral Hepatitis Research Laboratory, NHTMRI Cairo, Egypt
3Natinal Cancer Institute Cairo, Egypt
4Clinical Proteomics Program, NCI/FDA, Center for Biologics Evaluation, FDA USA
5SAIC-Frederick and Biomedical Proteomics Program, NCI Frederick, MD, USA

*To whom correspondence should be addressed.

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

Contact: hwr{at}georgetown.edu

Received on May 24, 2005; revised on August 30, 2005; accepted on September 8, 2005

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