Identifying potential biomarkers in LC-MS data

被引：15
|
作者：
Daszykowski, M.
Wu, W.
Nicholls, A. W.
Ball, R. J.
Czekaj, T.
Walczak, B.
机构：
[1] Silesian Univ, Dept Chemometr, Inst Chem, PL-40006 Katowice, Poland
[2] GlaxoSmithKline, Bioinformat Sci, Stevenage SG1 2NY, Herts, England
[3] GlaxoSmithKline, Investigat Preclin Toxicol, Ware SG12 0DP, Herts, England
来源：
JOURNAL OF CHEMOMETRICS | 2007年 / 21卷 / 7-9期
关键词：
UVE-PLS; background removal; noise reduction; variable selection;
D O I：
10.1002/cem.1066
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
In this paper an application of the uninformative variable elimination-partial least squares (UVE-PLS) method extended by the Monte Carlo approach for selection of possible biomarkers from the liquid chromatography coupled with mass spectrometry (LC-MS) data is reported. The main challenge consists not in the chemometrics analysis of LC-MS data, but in the data organization. However, as demonstrated in our study, the selected variables are similar regardless of the data organization strategy. The best results are obtained for the standard normal variate (SNV) transformed data. Copyright (c) 2007 John Wiley & Sons, Ltd.
引用
收藏
页码：292 / 302
页数：11
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