Min-redundancy and max-relevance multi-view feature selection for predicting ovarian cancer survival using multi-omics data

Location
Deutsche Nationalbibliothek Frankfurt am Main
ISSN
1755-8794
Extent
Online-Ressource
Language
Englisch
Notes
online resource.

Bibliographic citation
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

Creator
EL-Manzalawy, Yasser
Contributor
Hsieh, Tsung-Yu
Shivakumar, Manu
Kim, Dokyoon
Honavar, Vasant
SpringerLink (Online service)

DOI
10.1186/s12920-018-0388-0
URN
urn:nbn:de:101:1-2018102923141192522334
Rights
Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
Last update
14.08.2025, 10:45 AM CEST

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Associated

  • EL-Manzalawy, Yasser
  • Hsieh, Tsung-Yu
  • Shivakumar, Manu
  • Kim, Dokyoon
  • Honavar, Vasant
  • SpringerLink (Online service)

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