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Machine learning approach for discrimination of genotypes based on bright-field cellular images.
Suzuki, Godai; Saito, Yutaka; Seki, Motoaki; Evans-Yamamoto, Daniel; Negishi, Mikiko; Kakoi, Kentaro; Kawai, Hiroki; Landry, Christian R; Yachie, Nozomu; Mitsuyama, Toutai.
Afiliación
  • Suzuki G; Artificial Intelligence Research Center, National Institute of Advanced Industrial Science and Technology (AIST), Tokyo, 135-0064, Japan.
  • Saito Y; Artificial Intelligence Research Center, National Institute of Advanced Industrial Science and Technology (AIST), Tokyo, 135-0064, Japan.
  • Seki M; AIST-Waseda University Computational Bio Big-Data Open Innovation Laboratory (CBBD-OIL), Tokyo, 169-8555, Japan.
  • Evans-Yamamoto D; Graduate School of Frontier Sciences, The University of Tokyo, Chiba, 277-8561, Japan.
  • Negishi M; Research Center for Advanced Science and Technology, The University of Tokyo, Tokyo, 153-8904, Japan.
  • Kakoi K; Research Center for Advanced Science and Technology, The University of Tokyo, Tokyo, 153-8904, Japan.
  • Kawai H; Institute for Advanced Biosciences, Keio University, Tsuruoka, 997-0035, Japan.
  • Landry CR; Systems Biology Program, Graduate School of Media and Governance, Keio University, Fujisawa, 252-0882, Japan.
  • Yachie N; Research Center for Advanced Science and Technology, The University of Tokyo, Tokyo, 153-8904, Japan.
  • Mitsuyama T; Research Center for Advanced Science and Technology, The University of Tokyo, Tokyo, 153-8904, Japan.
NPJ Syst Biol Appl ; 7(1): 31, 2021 07 21.
Article en En | MEDLINE | ID: mdl-34290253
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Aprendizaje Automático Tipo de estudio: Prognostic_studies Idioma: En Revista: NPJ Syst Biol Appl Año: 2021 Tipo del documento: Article País de afiliación: Japón
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Aprendizaje Automático Tipo de estudio: Prognostic_studies Idioma: En Revista: NPJ Syst Biol Appl Año: 2021 Tipo del documento: Article País de afiliación: Japón