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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.
Affiliation
  • 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 in En | MEDLINE | ID: mdl-32376840
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Full text: 1 Database: MEDLINE Main subject: Drug Screening Assays, Antitumor / Computational Biology / Machine Learning / Antineoplastic Agents Type of study: Prognostic_studies Limits: Humans Language: En Year: 2020 Type: Article
Fulltext
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PubMed Links
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Full text: 1 Database: MEDLINE Main subject: Drug Screening Assays, Antitumor / Computational Biology / Machine Learning / Antineoplastic Agents Type of study: Prognostic_studies Limits: Humans Language: En Year: 2020 Type: Article