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Bioinformatics Advance Access published online on September 13, 2005
Bioinformatics, 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@oxfordjournals.org
Received May 24, 2005
Revised August 30, 2005
Accepted September 8, 2005

Article

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 3, Thomas P. Conrads 4, Timothy D. Veenstra 4, Christopher A. Loffredo 1, and Radoslav Goldman 1

1 Lombardi Comprehensive Cancer Center, Georgetown University, Washington, DC, USA
2 Viral Hepatitis Research Laboratory, NHTMRI, Cairo, Egypt
3 National Cancer Institute, Cairo, Egypt
4 Clinical Proteomics Program, NCI/FDA, Center for Biologics Evaluation, FDA, USA

* To whom correspondence should be addressed.
Habtom W. Ressom, E-mail: hwr{at}georgetown.edu


  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 discovery of proteins and protein-profiles that distinguish disease states, e.g. cancer patients from healthy individuals. We present low-level methods for 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 HWR's lab website at http://lombardi.georgetown.edu/labpage.


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