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1 Ergebnisse
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Machine learning classifier approaches for predicting respo..:
Ferrato, Mauricio H
;
Marsh, Adam G
;
Franke, Karl R
...
Mauricio H Ferrato and others, Machine learning classifier approaches for predicting response to RTK-type-III inhibitors demonstrate high accuracy using transcriptomic signatures and ex vivo data, Bioinformatics Advances, Volume 3, Issue 1, 2023, vbad034, https://doi.org/10.1093/bioadv/vbad034. , 2023
Link:
https://udspace.udel.edu/handle/19716/32841
RT Journal T1 <Title>Machine learning classifier approaches for predicting response to RTK-type-III inhibitors demonstrate high accuracy using transcriptomic signatures and ex vivo data</Title> UL https://suche.suub.uni-bremen.de/peid=base-ftunivdelaware:oai:udspace.udel.edu:19716_32841&Exemplar=1&LAN=DE A1 Ferrato, Mauricio H A1 Marsh, Adam G A1 Franke, Karl R A1 Huang, Benjamin J A1 Kolb, E. Anders A1 DeRyckere, Deborah A1 Grahm, Douglas K A1 Chandrasekaran, Sunita A1 Crowgey, Erin L PB Bioinformatics Advances YR 2023 JF Mauricio H Ferrato and others, Machine learning classifier approaches for predicting response to RTK-type-III inhibitors demonstrate high accuracy using transcriptomic signatures and ex vivo data, Bioinformatics Advances, Volume 3, Issue 1, 2023, vbad034, https://doi.org/10.1093/bioadv/vbad034 LK http://dx.doi.org/https://udspace.udel.edu/handle/19716/32841 DO https://udspace.udel.edu/handle/19716/32841 SF ELIB - SuUB Bremen
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