Ant colony optimization for biomarker identification from MALDI-TOF mass spectra.
We present a novel method that combines ant colony optimization with support vector machines (ACO-SVM) to select candidate biomarkers from MALDI-TOF serum profiles of hepatocellular carcinoma (HCC) patients and matched controls. The method identified relevant mass points that achieve high sensitivity and specificity in distinguishing HCC patients from healthy individuals. The results indicate that the MALDI-TOF technology could provide the means to discover novel biomarkers for HCC.
Ressom
Habtom W
HW
Lombardi Comprehensive Cancer Center, Georgetown Univ., Washington, DC, USA. hwr@georgetown.edu
Varghese
Rency S
RS
Orvisky
Eduard
E
Drake
Steven K
SK
Hortin
Glen L
GL
Abdel-Hamid
Mohamed
M
Loffredo
Christopher A
CA
Goldman
Radoslav
R
D000818:Animals; D001000:Ants; D015415:Biomarkers / Q000737:chemistry*; D014408:Biomarkers, Tumor / Q000737:chemistry*; D006528:Carcinoma, Hepatocellular / Q000175:diagnosis / Q000378:metabolism*; D006801:Humans; D013058:Mass Spectrometry; D010455:Peptides / Q000737:chemistry; D010675:Pheromones; D012372:ROC Curve; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted; D012984:Software; D019032:Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization / Q000295:instrumentation* / Q000379:methods*
D015415:Biomarkers; D014408:Biomarkers, Tumor; D010455:Peptides; D010675:Pheromones