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Few-shot learning creates predictive models of drug response that translate from high-throughput screens to individual patients.
Ma, Jianzhu; Fong, Samson H; Luo, Yunan; Bakkenist, Christopher J; Shen, John Paul; Mourragui, Soufiane; Wessels, Lodewyk F A; Hafner, Marc; Sharan, Roded; Peng, Jian; Ideker, Trey.
Afiliação
  • Ma J; Department of Medicine, University of California San Diego, La Jolla, CA, USA.
  • Fong SH; Department of Computer Science, Purdue University, West Lafayette, IN, USA.
  • Luo Y; Department of Medicine, University of California San Diego, La Jolla, CA, USA.
  • Bakkenist CJ; Department of Bioengineering, University of California San Diego, La Jolla, CA, USA.
  • Shen JP; Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL, USA.
  • Mourragui S; Department of Radiation Oncology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA.
  • Wessels LFA; Department of Gastrointestinal Medical Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, USA.
  • Hafner M; Division of Molecular Carcinogenesis, Oncode Institute, The Netherlands Cancer Institute, Amsterdam, the Netherlands.
  • Sharan R; Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, Delft, the Netherlands.
  • Peng J; Division of Molecular Carcinogenesis, Oncode Institute, The Netherlands Cancer Institute, Amsterdam, the Netherlands.
  • Ideker T; Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, Delft, the Netherlands.
Nat Cancer ; 2(2): 233-244, 2021 02.
Article em En | MEDLINE | ID: mdl-34223192
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Redes Neurais de Computação / Aprendizado de Máquina Tipo de estudo: Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Revista: Nat Cancer Ano de publicação: 2021 Tipo de documento: Article País de afiliação: Estados Unidos País de publicação: Reino Unido
Texto completo
Adicionar na Minha BVS
Imprimir
XML
PubMed Links
Buscar no Google

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Redes Neurais de Computação / Aprendizado de Máquina Tipo de estudo: Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Revista: Nat Cancer Ano de publicação: 2021 Tipo de documento: Article País de afiliação: Estados Unidos País de publicação: Reino Unido