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Verfasst von:Tanevski, Jovan [VerfasserIn]   i
 Ramirez Flores, Ricardo O. [VerfasserIn]   i
 Gabor, Attila [VerfasserIn]   i
 Schapiro, Denis [VerfasserIn]   i
 Sáez Rodríguez, Julio [VerfasserIn]   i
Titel:Explainable multiview framework for dissecting spatial relationships from highly multiplexed data
Verf.angabe:Jovan Tanevski, Ricardo Omar Ramirez Flores, Attila Gabor, Denis Schapiro and Julio Saez-Rodriguez
E-Jahr:2022
Jahr:14 April 2022
Umfang:31 S.
Fussnoten:Gesehen am 17.05.2022
Titel Quelle:Enthalten in: Genome biology
Ort Quelle:London : BioMed Central, 2000
Jahr Quelle:2022
Band/Heft Quelle:23(2022), Artikel-ID 97, Seite 1-31
ISSN Quelle:1474-760X
Abstract:The advancement of highly multiplexed spatial technologies requires scalable methods that can leverage spatial information. We present MISTy, a flexible, scalable, and explainable machine learning framework for extracting relationships from any spatial omics data, from dozens to thousands of measured markers. MISTy builds multiple views focusing on different spatial or functional contexts to dissect different effects. We evaluated MISTy on in silico and breast cancer datasets measured by imaging mass cytometry and spatial transcriptomics. We estimated structural and functional interactions coming from different spatial contexts in breast cancer and demonstrated how to relate MISTy’s results to clinical features.
DOI:doi:10.1186/s13059-022-02663-5
URL:kostenfrei: Volltext: https://doi.org/10.1186/s13059-022-02663-5
 DOI: https://doi.org/10.1186/s13059-022-02663-5
Datenträger:Online-Ressource
Sprache:eng
Sach-SW:Intercellular signaling
 Machine learning
 Multiplexed data
 Spatial omics
K10plus-PPN:1802139761
Verknüpfungen:→ Zeitschrift
 
 
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