Unsupervised machine learning for exploratory data analysis in imaging mass spectrometry

Imaging mass spectrometry (IMS) is a rapidly advancing molecular imaging modality that can map the spatial distribution of molecules with high chemical specificity. IMS does not require prior tagging of molecular targets and is able to measure a large number of ions concurrently in a single experime...

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Vydáno v:	Mass spectrometry reviews Ročník 39; číslo 3; s. 245 - 291
Hlavní autoři:	Verbeeck, Nico, Caprioli, Richard M., Van de Plas, Raf
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	United States Wiley Subscription Services, Inc 01.05.2020 John Wiley and Sons Inc
Témata:	Animals clustering Computer applications Data Analysis Data processing DESI Desorption Humans imaging mass spectrometry Ions LAESI LAICP Learning algorithms Machine learning MALDI manifold learning Mass spectrometry Mass Spectrometry - methods Mass spectroscopy matrix factorization Molecular Imaging - methods Review Scientific imaging SIMS Spatial distribution unsupervised Unsupervised Machine Learning matrix factorization imaging mass spectrometry data analysis MALDI unsupervised DESI SIMS clustering LAESI LAICP machine learning manifold learning
ISSN:	0277-7037, 1098-2787, 1098-2787
On-line přístup:	Získat plný text
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