Analysis of mass spectral serum profiles for biomarker selection.
BACKGROUND
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.


BACKGROUND
MATLAB scripts to implement the methods described in this paper are available from the HWR's lab website http://lombardi.georgetown.edu/labpage
Ressom
Habtom W
HW
Lombardi Comprehensive Cancer Center, Georgetown University, Washington, DC, USA. hwr@georgetown.edu
Varghese
Rency S
RS
Abdel-Hamid
Mohamed
M
Eissa
Sohair Abdel-Latif
SA
Saha
Daniel
D
Goldman
Lenka
L
Petricoin
Emanuel F
EF
Conrads
Thomas P
TP
Veenstra
Timothy D
TD
Loffredo
Christopher A
CA
Goldman
Radoslav
R
D000465:Algorithms; D001185:Artificial Intelligence; D014408:Biomarkers, Tumor / Q000097:blood*; D001774:Blood Chemical Analysis / Q000379:methods*; D003936:Diagnosis, Computer-Assisted / Q000379:methods; D020869:Gene Expression Profiling / Q000379:methods; D006801:Humans; D008113:Liver Neoplasms / Q000097:blood* / Q000175:diagnosis*; D009363:Neoplasm Proteins / Q000097:blood*; D010449:Peptide Mapping / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D019032:Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization / Q000379:methods*
D014408:Biomarkers, Tumor; D009363:Neoplasm Proteins