Performance Evaluation of Algorithms for the Classification of Metabolic 1H NMR Fingerprints
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- 作者：Jochen Hochrein ; Matthias S. Klein ; Helena U. Zacharias ; Juan Li ; Gene Wijffels ; Horst Joachim Schirra ; Rainer Spang ; Peter J. Oefner ; Wolfram Gronwald
- 刊名：Journal of Proteome Research
- 出版年：2012
- 出版时间：December 7, 2012
- 年：2012
- 卷：11
- 期：12
- 页码：6242-6251
- 全文大小：328K
- 年卷期：v.11,no.12(December 7, 2012)
- ISSN：1535-3907
文摘
Nontargeted metabolite fingerprinting is increasingly applied to biomedical classification. The choice of classification algorithm may have a considerable impact on outcome. In this study, employing nested cross-validation for assessing predictive performance, six binary classification algorithms in combination with different strategies for data-driven feature selection were systematically compared on five data sets of urine, serum, plasma, and milk one-dimensional fingerprints obtained by proton nuclear magnetic resonance (NMR) spectroscopy. Support Vector Machines and Random Forests combined with t-score-based feature filtering performed well on most data sets, whereas the performance of the other tested methods varied between data sets.