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A novel machine learning approach reveals latent vascular phenotypes predictive of renal cancer outcome.
Ing, Nathan; Huang, Fangjin; Conley, Andrew; You, Sungyong; Ma, Zhaoxuan; Klimov, Sergey; Ohe, Chisato; Yuan, Xiaopu; Amin, Mahul B; Figlin, Robert; Gertych, Arkadiusz; Knudsen, Beatrice S.
Afiliación
  • Ing N; Department of Surgery, Cedars Sinai Medical Center, Los Angeles, CA, USA.
  • Huang F; Department of Biomedical Sciences, Cedars Sinai Medical Center, Los Angeles, CA, USA.
  • Conley A; Department of Biomedical Sciences, Cedars Sinai Medical Center, Los Angeles, CA, USA.
  • You S; Department of Biomedical Sciences, Cedars Sinai Medical Center, Los Angeles, CA, USA.
  • Ma Z; Department of Surgery, Cedars Sinai Medical Center, Los Angeles, CA, USA.
  • Klimov S; Department of Biomedical Sciences, Cedars Sinai Medical Center, Los Angeles, CA, USA.
  • Ohe C; Department of Biomedical Sciences, Cedars Sinai Medical Center, Los Angeles, CA, USA.
  • Yuan X; Department of Pathology, Cedars Sinai Medical Center, Los Angeles, CA, USA.
  • Amin MB; Department of Biomedical Sciences, Cedars Sinai Medical Center, Los Angeles, CA, USA.
  • Figlin R; Department of Pathology, Cedars Sinai Medical Center, Los Angeles, CA, USA.
  • Gertych A; Samuel Oschin Comprehensive Cancer Institute, Cedars Sinai Medical Center, Los Angeles, CA, USA.
  • Knudsen BS; Department of Surgery, Cedars Sinai Medical Center, Los Angeles, CA, USA. Arkadiusz.Gertych@cshs.org.
Sci Rep ; 7(1): 13190, 2017 10 16.
Article en En | MEDLINE | ID: mdl-29038551
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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Aprendizaje Automático / Neoplasias Renales Tipo de estudio: Prognostic_studies / Risk_factors_studies Límite: Humans Idioma: En Revista: Sci Rep Año: 2017 Tipo del documento: Article País de afiliación: Estados Unidos
Texto completo
Imprimir
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PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Aprendizaje Automático / Neoplasias Renales Tipo de estudio: Prognostic_studies / Risk_factors_studies Límite: Humans Idioma: En Revista: Sci Rep Año: 2017 Tipo del documento: Article País de afiliación: Estados Unidos