Affiliations: Laboratory of Proteomics and Analytical Technologies,
SAIC-Frederick, Inc., NCI Frederick, Frederick, MD, USA | Advanced Biomedical Computer Center, SAIC-Frederick
Inc., NCI-Frederick, Frederick, MD, USA | Division of Infectious Diseases, Department of
Medicine, University of Maryland School of Medicine, Baltimore, MD 21201,
USA | Research Service, VA Maryland Health Care System,
Baltimore, MD 21201, USA
Note: [] Corresponding authot: Dr. Susan Keay, VA Medical Center, Room
3B-184, 10 N. Greene Street, Baltimore, MD 21201, USA. TeL.: +1 410 605 7000
ext. 6450; Fax: +1 410 605 7837; E-mail: [email protected]
Abstract: The advent of systems biology approaches that have stemmed from the
sequencing of the human genome has led to the search for new methods to
diagnose diseases. While much effort has been focused on the identification of
disease-specific biomarkers, recent efforts are underway toward the use of
proteomic and metabonomic patterns to indicate disease. We have developed and
contrasted the use of both proteomic and metabonomic patterns in urine for the
detection of interstitial cystitis (IC). The methodology relies on advanced
bioinformatics to scrutinize information contained within mass spectrometry
(MS) and high-resolution proton nuclear magnetic resonance
(^1H-NMR) spectral patterns to distinguish IC-affected from
non-affected individuals as well as those suffering from bacterial cystitis
(BC). We have applied a novel pattern recognition tool that employs an
unsupervised system (self-organizing-type cluster mapping) as a fitness test
for a supervised system (a genetic algorithm). With this approach, a training
set comprised of mass spectra and ^1H-NMR spectra from urine
derived from either unaffected individuals or patients with IC is employed so
that the most fit combination of relative, normalized intensity features
defined at precise m/z or chemical shift values plotted in $n$-space can
reliably distinguish the cohorts used in training. Using this bioinformatic
approach, we were able to discriminate spectral patterns associated with
IC-affected, BC-affected, and unaffected patients with a success rate of
approximately 84%.
Journal: Disease Markers, vol. 19, no. 4-5, pp. 169-183, 2003,2004
