EmbryoNet : using deep learning to link embryonic phenotypes to signaling pathways
- Standort
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Deutsche Nationalbibliothek Frankfurt am Main
- Umfang
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Online-Ressource
- Sprache
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Englisch
- Erschienen in
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In: Nature Methods ; 20 (2023). - S. 815-823. - Springer. - ISSN 1548-7091. - eISSN 1548-7105
- Klassifikation
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Biowissenschaften, Biologie
- Ereignis
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Veröffentlichung
- (wo)
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Konstanz
- (wer)
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KOPS Universität Konstanz
- (wann)
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2023
- Urheber
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Capek, Daniel
Safroshkin, Matvey
Morales-Navarrete, Hernán
Toulany, Nikan
Arutyunov, Grigory
Kurzbach, Anica
Bihler, Johanna
Hagauer, Julia
Jordan, Ben
Müller, Patrick
- URN
-
urn:nbn:de:bsz:352-2-1xp3ftm28rrew5
- Rechteinformation
-
Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Letzte Aktualisierung
-
25.03.2025, 13:51 MEZ
Datenpartner
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Beteiligte
- Capek, Daniel
- Safroshkin, Matvey
- Morales-Navarrete, Hernán
- Toulany, Nikan
- Arutyunov, Grigory
- Kurzbach, Anica
- Bihler, Johanna
- Hagauer, Julia
- Jordan, Ben
- Müller, Patrick
- KOPS Universität Konstanz
Entstanden
- 2023
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