Hochrein, Jochen and Klein, Matthias S. and Zacharias, Helena U. and Li, Juan and Wijffels, Gene and Schirra, Horst Joachim and Spang, Rainer and Oefner, Peter J. and Gronwald, Wolfram (2012) Performance Evaluation of Algorithms for the Classification of Metabolic H-1 NMR Fingerprints. JOURNAL OF PROTEOME RESEARCH, 11 (12). pp. 6242-6251. ISSN 1535-3893, 1535-3907
Full text not available from this repository. (Request a copy)Abstract
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.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | GENE-EXPRESSION DATA; MASS-SPECTROMETRY; FEATURE-SELECTION; MICROARRAY DATA; DATA SETS; NMR; SPECTROSCOPY; DISEASE; DISCRIMINATION; DIAGNOSIS; metabolomics; NMR; classification; cross-validation; fingerprinting |
| Subjects: | 500 Science > 570 Life sciences |
| Divisions: | Medicine > Institut für Funktionelle Genomik > Lehrstuhl für Funktionelle Genomik (Prof. Oefner) |
| Depositing User: | Dr. Gernot Deinzer |
| Date Deposited: | 04 May 2020 05:31 |
| Last Modified: | 04 May 2020 05:31 |
| URI: | https://pred.uni-regensburg.de/id/eprint/17674 |
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