Discovery of metabolite features for the modelling and analysis of high-resolution NMR spectra
This study presents three feature selection methods for identifying the metabolite features in nuclear magnetic resonance spectra that contribute to the distinction of samples among varying nutritional conditions. Principal component analysis, Fisher discriminant analysis, and Partial Least Square Discriminant Analysis (PLS-DA) were used to calculate the importance of individual metabolite feature in spectra. Moreover, an Orthogonal Signal Correction (OSC) filter was used to eliminate unnecessary variations in spectra. We evaluated the presented methods by comparing the ability of classification based on the features selected by each method. The result showed that the best classification was achieved from an OSC-PLS-DA model.
| Medienart: |
Artikel |
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| Erscheinungsjahr: |
2008 |
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| Erschienen: |
2008 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:2 |
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| Enthalten in: |
International journal of data mining and bioinformatics - 2(2008), 2 vom: 16., Seite 176-92 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Cho, Hyun-Woo [VerfasserIn] |
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| Themen: |
Comparative Study |
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| Anmerkungen: |
Date Completed 14.10.2008 Date Revised 20.10.2021 published: Print Citation Status MEDLINE |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM182197794 |
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| 100 | 1 | |a Cho, Hyun-Woo |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Discovery of metabolite features for the modelling and analysis of high-resolution NMR spectra |
| 264 | 1 | |c 2008 | |
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| 500 | |a Date Completed 14.10.2008 | ||
| 500 | |a Date Revised 20.10.2021 | ||
| 500 | |a published: Print | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a This study presents three feature selection methods for identifying the metabolite features in nuclear magnetic resonance spectra that contribute to the distinction of samples among varying nutritional conditions. Principal component analysis, Fisher discriminant analysis, and Partial Least Square Discriminant Analysis (PLS-DA) were used to calculate the importance of individual metabolite feature in spectra. Moreover, an Orthogonal Signal Correction (OSC) filter was used to eliminate unnecessary variations in spectra. We evaluated the presented methods by comparing the ability of classification based on the features selected by each method. The result showed that the best classification was achieved from an OSC-PLS-DA model | ||
| 650 | 4 | |a Comparative Study | |
| 650 | 4 | |a Evaluation Study | |
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Research Support, N.I.H., Extramural | |
| 650 | 4 | |a Research Support, U.S. Gov't, Non-P.H.S. | |
| 650 | 7 | |a Proteome |2 NLM | |
| 700 | 1 | |a Kim, Seoung Bum |e verfasserin |4 aut | |
| 700 | 1 | |a Jeong, Myong K |e verfasserin |4 aut | |
| 700 | 1 | |a Park, Youngja |e verfasserin |4 aut | |
| 700 | 1 | |a Miller, Nana Gletsu |e verfasserin |4 aut | |
| 700 | 1 | |a Ziegler, Thomas R |e verfasserin |4 aut | |
| 700 | 1 | |a Jones, Dean P |e verfasserin |4 aut | |
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