Application of the random forest classification algorithm to a SELDI-TOF proteomics study in the setting of a cancer prevention trial.
A thorough discussion of the random forest (RF) algorithm as it relates to a SELDI-TOF proteomics study is presented, with special emphasis on its application for cancer prevention: specifically, what makes it an efficient, yet reliable classifier, and what makes it optimal among the many available approaches. The main body of the paper treats the particulars of how to successfully apply the RF algorithm in a proteomics profiling study to construct a classifier and discover peak intensities most likely responsible for the separation between the classes.
Izmirlian
Grant
G
Biometry Research Group, Division of Cancer Prevention, National Cancer Institute, National Institutes of Health, DHHS, Executive Plaza North, Suite 3131, 6130 Executive Boulevard, MSC 7354, Bethesda, MD 20852, USA. izmirlian@nih.gov
D000465:Algorithms; D006801:Humans; D009369:Neoplasms / Q000145:classification / Q000235:genetics / Q000517:prevention & control*; D040901:Proteomics / Q000379:methods*; D015203:Reproducibility of Results; D019032:Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization / Q000379:methods