Min-redundancy and max-relevance multi-view feature selection for predicting ovarian cancer survival using multi-omics data
- Standort
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Deutsche Nationalbibliothek Frankfurt am Main
- ISSN
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1755-8794
- Umfang
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Online-Ressource
- Sprache
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Englisch
- Anmerkungen
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online resource.
- Erschienen in
-
Min-redundancy and max-relevance multi-view feature selection for predicting ovarian cancer survival using multi-omics data ; volume:11 ; number:3 ; day:14 ; month:9 ; year:2018 ; pages:19-31 ; date:9.2018
BMC medical genomics ; 11, Heft 3 (14.9.2018), 19-31, 9.2018
- Urheber
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EL-Manzalawy, Yasser
- Beteiligte Personen und Organisationen
- DOI
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10.1186/s12920-018-0388-0
- URN
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urn:nbn:de:101:1-2018102923141192522334
- Rechteinformation
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Letzte Aktualisierung
-
14.08.2025, 10:45 MESZ
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Beteiligte
- EL-Manzalawy, Yasser
- Hsieh, Tsung-Yu
- Shivakumar, Manu
- Kim, Dokyoon
- Honavar, Vasant
- SpringerLink (Online service)
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