Premium
Application of the Random Forest Classification Algorithm to a SELDI‐TOF Proteomics Study in the Setting of a Cancer Prevention Trial
Author(s) -
IZMIRLIAN GRANT
Publication year - 2004
Publication title -
annals of the new york academy of sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.712
H-Index - 248
eISSN - 1749-6632
pISSN - 0077-8923
DOI - 10.1196/annals.1310.015
Subject(s) - random forest , classifier (uml) , proteomics , computer science , profiling (computer programming) , algorithm , artificial intelligence , machine learning , chemistry , biochemistry , gene , operating system
A bstract : 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.