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Titel:

Feature Selection Pipelines with Classification for Non-targeted Metabolomics Combining the Neural Network and Genetic Algorithm.

Dokumenttyp:
Journal Article; Research Support, Non-U.S. Gov't
Autor(en):
Lisitsyna, Anna; Moritz, Franco; Liu, Youzhong; Al Sadat, Loubna; Hauner, Hans; Claussnitzer, Melina; Schmitt-Kopplin, Philippe; Forcisi, Sara
Abstract:
Non-targeted metabolomics via high-resolution mass spectrometry methods, such as direct infusion Fourier transform-ion cyclotron resonance mass spectrometry (DI-FT-ICR MS), produces data sets with thousands of features. By contrast, the number of samples is in general substantially lower. This disparity presents challenges when analyzing non-targeted metabolomics data sets and often requires custom methods to uncover information not always accessible via classical statistical techniques. In this...     »
Zeitschriftentitel:
Anal Chem
Jahr:
2022
Band / Volume:
94
Heft / Issue:
14
Seitenangaben Beitrag:
5474-5482
Volltext / DOI:
doi:10.1021/acs.analchem.1c03237
PubMed:
http://view.ncbi.nlm.nih.gov/pubmed/35344349
Print-ISSN:
0003-2700
TUM Einrichtung:
Else Kröner-Fresenius-Zentrum für Ernährungsmedizin - Klinik für Ernährungsmedizin
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