计算机科学
数据挖掘
可靠性(半导体)
置信区间
交叉验证
预测建模
人工智能
机器学习
样本量测定
度量(数据仓库)
样品(材料)
表达式(计算机科学)
统计
数学
功率(物理)
物理
化学
色谱法
量子力学
程序设计语言
出处
期刊:PLOS ONE
[Public Library of Science]
日期:2010-11-23
卷期号:5 (11): e15543-e15543
被引量:256
标识
DOI:10.1371/journal.pone.0015543
摘要
Gene-expression signature-based disease classification and clinical outcome prediction has not been widely introduced in clinical medicine as initially expected, mainly due to the lack of extensive validation needed for its clinical deployment. Obstacles include variable measurement in microarray assay, inconsistent assay platform, analytical requirement for comparable pair of training and test datasets, etc. Furthermore, as medical device helping clinical decision making, the prediction needs to be made for each single patient with a measure of its reliability. To address these issues, there is a need for flexible prediction method less sensitive to difference in experimental and analytical conditions, applicable to each single patient, and providing measure of prediction confidence. The nearest template prediction (NTP) method provides a convenient way to make class prediction with assessment of prediction confidence computed in each single patient's gene-expression data using only a list of signature genes and a test dataset. We demonstrate that the method can be flexibly applied to cross-platform, cross-species, and multiclass predictions without any optimization of analysis parameters.
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