期刊
TRAC-TRENDS IN ANALYTICAL CHEMISTRY
卷 38, 期 -, 页码 154-162出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.trac.2011.11.007
关键词
Algorithm; Bioinformatics; Chemometrics; Complex analytical system; Data modeling; Modeling; Model-population analysis (MPA); Monte Carlo sampling; Outlier detection; Variable selection
资金
- National Nature Foundation Committee of PR China [20875104, 21075138]
- Graduate Degree Thesis Innovation Foundation of Central South University [CX2010B057]
Model-population analysis (MPA) was recently proposed as a general framework for designing new types of chemometrics and bioinformatics algorithms, and it has found promising applications in chemistry and biology. The goal of MPA is to extract useful information from complex analytical systems, so as to lead to better understanding and better modeling of chemical and biological data. To give an overall picture of MPA, we first review its key elements. Then, we describe the theories and the applications of selected methods that focus on the two fundamental aspects in chemical and biological modeling: outlier detection and variable selection. We highlight the key common principles of these methods and pinpoint the critical differences underlying each method. (c) 2012 Elsevier Ltd. All rights reserved.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据