Application of the random forest classification algorithm to a SELDI-TOF proteomics study in the setting of a cancer prevention trial
Application of the random forest classification algorithm to a SELDI-TOF proteomics study in the setting of a cancer prevention trial
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DOI：
10.1196/annals.1310.015
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发表时间：
2004-01-01
期刊：
影响因子：
--
通讯作者：
Izmirlian, G
中科院分区：
文献类型：
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作者：
Izmirlian, G
对随机森林（RF）算法进行了深入的讨论，因为它与SELDI-TOF蛋白质组学研究有关，特别强调了它在癌症预防方面的应用：具体来说，是什么使它成为一种有效而可靠的分类器，以及是什么使它在许多可用的方法中成为最佳的。本文的主体处理了如何成功地将RF算法应用于蛋白质组学分析研究中的细节，以构建分类器并发现最有可能导致类之间分离的峰值强度。
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.