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Robotic data acquisition with deep learning enables cell image-based prediction of transcriptomic phenotypes.
Jin, Jianshi; Ogawa, Taisaku; Hojo, Nozomi; Kryukov, Kirill; Shimizu, Kenji; Ikawa, Tomokatsu; Imanishi, Tadashi; Okazaki, Taku; Shiroguchi, Katsuyuki.
Affiliation
  • Jin J; Laboratory for Prediction of Cell Systems Dynamics, RIKEN Center for Biosystems Dynamics Research (BDR), Suita, Osaka 565-0874, Japan.
  • Ogawa T; Laboratory for Prediction of Cell Systems Dynamics, RIKEN Center for Biosystems Dynamics Research (BDR), Suita, Osaka 565-0874, Japan.
  • Hojo N; Laboratory for Prediction of Cell Systems Dynamics, RIKEN Center for Biosystems Dynamics Research (BDR), Suita, Osaka 565-0874, Japan.
  • Kryukov K; Department of Molecular Life Science, Biomedical Informatics Laboratory, Tokai University School of Medicine, Isehara, Kanagawa 259-1193, Japan.
  • Shimizu K; Laboratory of Molecular Immunology, Institute for Quantitative Biosciences, The University of Tokyo, Bunkyo-ku, Tokyo 113-0032, Japan.
  • Ikawa T; Division of Immunology and Allergy, Research Institute for Biomedical Sciences, Tokyo University of Science, Noda, Chiba 278-0022, Japan.
  • Imanishi T; Department of Molecular Life Science, Biomedical Informatics Laboratory, Tokai University School of Medicine, Isehara, Kanagawa 259-1193, Japan.
  • Okazaki T; Laboratory of Molecular Immunology, Institute for Quantitative Biosciences, The University of Tokyo, Bunkyo-ku, Tokyo 113-0032, Japan.
  • Shiroguchi K; Laboratory for Prediction of Cell Systems Dynamics, RIKEN Center for Biosystems Dynamics Research (BDR), Suita, Osaka 565-0874, Japan.
Proc Natl Acad Sci U S A ; 120(1): e2210283120, 2023 01 03.
Article in En | MEDLINE | ID: mdl-36577074
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Robotics / Robotic Surgical Procedures / Deep Learning Type of study: Prognostic_studies / Risk_factors_studies Language: En Journal: Proc Natl Acad Sci U S A Year: 2023 Document type: Article Affiliation country: Japan Country of publication: United States
Fulltext
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Robotics / Robotic Surgical Procedures / Deep Learning Type of study: Prognostic_studies / Risk_factors_studies Language: En Journal: Proc Natl Acad Sci U S A Year: 2023 Document type: Article Affiliation country: Japan Country of publication: United States