Application of the Bayesian MMSE estimator for classification error to gene expression microarray data

被引:26
|
作者
Dalton, Lori A. [1 ]
Dougherty, Edward R. [1 ,2 ,3 ]
机构
[1] Texas A&M Univ, Dept Elect & Comp Engn, College Stn, TX 77843 USA
[2] Translat Genom Res Inst, Computat Biol Div, Phoenix, AZ 85004 USA
[3] Univ Texas MD Anderson Canc Ctr, Dept Bioinformat & Computat Biol, Houston, TX 77030 USA
基金
美国国家科学基金会;
关键词
FEATURE-SELECTION METHODS; DISCRETE CLASSIFICATION; PERFORMANCE; NORMALITY; MODELS;
D O I
10.1093/bioinformatics/btr272
中图分类号
Q5 [生物化学];
学科分类号
071010 ; 081704 ;
摘要
Motivation: With the development of high-throughput genomic and proteomic technologies, coupled with the inherent difficulties in obtaining large samples, biomedicine faces difficult small-sample classification issues, in particular, error estimation. Most popular error estimation methods are motivated by intuition rather than mathematical inference. A recently proposed error estimator based on Bayesian minimum mean square error estimation places error estimation in an optimal filtering framework. In this work, we examine the application of this error estimator to gene expression microarray data, including the suitability of the Gaussian model with normal-inverse-Wishart priors and how to find prior probabilities. Results: We provide an implementation for non-linear classification, where closed form solutions are not available. We propose a methodology for calibrating normal-inverse-Wishart priors based on discarded microarray data and examine the performance on synthetic high-dimensional data and a real dataset from a breast cancer study. The calibrated Bayesian error estimator has superior root mean square performance, especially with moderate to high expected true errors and small feature sizes.
引用
收藏
页码:1822 / 1831
页数:10
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