Application of the random forest classification algorithm to a SELDI-TOF proteomics study in the setting of a cancer prevention trial

被引:90
作者
Izmirlian, G [1 ]
机构
[1] NCI, DHHS, Div Canc Prevent, Biometry Res Grp, Rockville, MD 20852 USA
来源
APPLICATIONS OF BIOINFORMATICS IN CANCER DETECTION | 2004年 / 1020卷
关键词
random forest (RF) algorithm; SELDI-TOF; cancer; classifier; classification tree (CT); detection; prevention; MDM; statistic;
D O I
10.1196/annals.1310.015
中图分类号
Q5 [生物化学];
学科分类号
071010 ; 081704 ;
摘要
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.
引用
收藏
页码:154 / 174
页数:21
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