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Discovering the hidden messages within cell trajectories using a deep learning approach for in vitro evaluation of cancer drug treatments.
Mencattini, A; Di Giuseppe, D; Comes, M C; Casti, P; Corsi, F; Bertani, F R; Ghibelli, L; Businaro, L; Di Natale, C; Parrini, M C; Martinelli, E.
Afiliação
  • Mencattini A; Department of Electronic Engineering, University of Rome Tor Vergata, Rome, Italy.
  • Di Giuseppe D; Department of Electronic Engineering, University of Rome Tor Vergata, Rome, Italy.
  • Comes MC; Department of Electronic Engineering, University of Rome Tor Vergata, Rome, Italy.
  • Casti P; Department of Electronic Engineering, University of Rome Tor Vergata, Rome, Italy.
  • Corsi F; Department of Chemical Science and Technologies, University of Rome Tor Vergata, Rome, Italy.
  • Bertani FR; Institute for Photonics and Nanotechnology, Italian National Research Council, 00156, Rome, Italy.
  • Ghibelli L; Department of Biology, University of Rome Tor Vergata, Rome, Italy.
  • Businaro L; Institute for Photonics and Nanotechnology, Italian National Research Council, 00156, Rome, Italy.
  • Di Natale C; Department of Electronic Engineering, University of Rome Tor Vergata, Rome, Italy.
  • Parrini MC; Institute Curie, Centre de Recherche, Paris Sciences et Lettres Research University, 75005, Paris, France.
  • Martinelli E; Department of Electronic Engineering, University of Rome Tor Vergata, Rome, Italy. martinelli@ing.uniroma2.it.
Sci Rep ; 10(1): 7653, 2020 05 06.
Article em En | MEDLINE | ID: mdl-32376840
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Texto completo: 1 Bases de dados: MEDLINE Assunto principal: Ensaios de Seleção de Medicamentos Antitumorais / Biologia Computacional / Aprendizado de Máquina / Antineoplásicos Tipo de estudo: Prognostic_studies Limite: Humans Idioma: En Revista: Sci Rep Ano de publicação: 2020 Tipo de documento: Article País de afiliação: Itália
Texto completo
Adicionar na Minha BVS
Imprimir
XML
PubMed Links
Buscar no Google

Texto completo: 1 Bases de dados: MEDLINE Assunto principal: Ensaios de Seleção de Medicamentos Antitumorais / Biologia Computacional / Aprendizado de Máquina / Antineoplásicos Tipo de estudo: Prognostic_studies Limite: Humans Idioma: En Revista: Sci Rep Ano de publicação: 2020 Tipo de documento: Article País de afiliação: Itália