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The '''Golm Metabolome Database (GMD)''' <ref>{{cite journal | title= Decision tree supported substructure prediction of metabolites from GC-MS profiles|author= Hummel J, Strehmel N, Selbig J, Walther D and Kopka J|journal= [[Metabolomics]] |volume=6 |pages=
== Background ==
Gas chromatography (GC) coupled to mass spectrometry (MS) is one of the most widespread routine technologies applied to the large scale screening and discovery of novel biomarkers in [[metabolomics]]. However, the majority of MSTs currently measured in plant [[metabolomic]] [[profiling]] experiments remains unidentified due to the lack of authenticated pure reference substances and the expensive and time-consuming effort to maintain mass spectral RI libraries required for compound identification by GC-MS.
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Aiming at the classification and identification of un-identified MSTs, the GMD accessed the information on available reference compounds as a source of training data to apply [[decision trees]] (DT) as a supervised [[machine learning]] approach. Single structure feature extraction was applied to classify the metabolite space of the GMD prior to DT training. DT-based prediction of the most frequent substructures classify low resolution GC-MS mass spectra of the linked (potentially unknown) metabolite with respect to the presence or absence of the chemical moieties.
The web-based frontend supports conventional mass spectral and RI comparison by ranked hit lists as well as advanced DT supported substructure prediction. Batch processing is enabled via [[Simple Object Access Protocol]] (SOAP)-based web services while web-based data access services expose particular data base entities adapting [[Representational State Transfer]] (ReST) principles and mass spectral standards such as [[NIST]]-MSP and [[JCAMP]]-DX.
==See
* [[Human Metabolome Database]]
* [[MassBank (database)|MassBank]]
==References==
{{reflist}}
==External links==
* [http://gmd.mpimp-golm.mpg.de GMD website]
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