Evaluation of ensemble Monte Carlo variable selection for identification of metabolite markers on NMR data
| Autor: | Gerard Downey, Carlos Esquerre, Aoife O'Gorman, Colm P. O'Donnell, Aoife Gowen |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Agaricus
Proton Magnetic Resonance Spectroscopy Metabolite Monte Carlo method Wine Feature selection Urine 01 natural sciences Biochemistry Analytical Chemistry chemistry.chemical_compound 0404 agricultural biotechnology Statistics Animals Environmental Chemistry Multiple correlation Spectroscopy 010401 analytical chemistry 04 agricultural and veterinary sciences Magnetic Resonance Imaging 040401 food science Nmr data Rats 0104 chemical sciences Identification (information) chemistry Proton NMR Biological system Monte Carlo Method Biomarkers |
| Zdroj: | Analytica Chimica Acta. 964:45-54 |
| ISSN: | 0003-2670 |
| DOI: | 10.1016/j.aca.2017.01.027 |
| Popis: | The aim of this study was to investigate the potential of the recently developed ensemble Monte Carlo Variable Selection (EMCVS) method to identify the relevant portions of high resolution 1H NMR spectra as a metabolite fingerprinting tool and compare to a widely used method (Variable importance on projection (VIP)) and recently proposed variable selected methods i.e. selectivity ratio (SR) and significance multivariate correlation (sMC). As case studies two quantitative publicly available datasets: wine samples, urine samples of rats, and an experiment on mushroom (Agaricus bisporus) were examined. EMCVS outperformed the three other variable selection methods in most cases, selecting fewer chemical shifts and leading to improved classification of mushrooms and prediction of onion by-products intake and wine components. These fewer chemical shift regions facilitate the interpretation of the NMR spectra, fingerprinting and identification of metabolite markers. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Full Text from ScienceDirect