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J-GLOBAL ID：201902256583432252   Reference number：19A1884874

Host Cell Prediction of Exosomes Using Morphological Features on Solid Surfaces Analyzed by Machine Learning

機械学習により解析した固体表面上の形態学的特徴を用いたエキソソームの宿主細胞予測【JST・京大機械翻訳】

Author (12)： Ito Kazuki (Yokohama National University, Japan) ,  Ogawa Yuta (Yokohama National University, Japan) ,  Yokota Keiji (Yokohama National University, Japan) ,  Matsumura Sachiko (Japanese Foundation for Cancer Research, Japan) ,  Minamisawa Tamiko (Japanese Foundation for Cancer Research, Japan) ,  Suga Kanako (Japanese Foundation for Cancer Research, Japan) ,  Shiba Kiyotaka (Japanese Foundation for Cancer Research, Japan) ,  Kimura Yasuo (Tokyo University of Technology, Japan) ,  Hirano-Iwata Ayumi (Tohoku University, Miyagi, Japan) ,  Takamura Yuzuru (Japan Advanced Institute of Science and Technology, Ishikawa, Japan) ,  Ogino Toshio (Yokohama National University, Japan) ,  Ogino Toshio (Japan Advanced Institute of Science and Technology, Ishikawa, Japan)
Material： Journal of Physical Chemistry B  (Journal of Physical Chemistry B. Biophysical Chemistry, Biomaterials, Liquids, and Soft Matter)
Volume： 122  Issue： 23  Page： 6224-6235  Publication year： 2018 
JST Material Number： W0921A  ISSN： 1520-6106  CODEN： JPCBFK  Document type： Article
Article type： 原著論文  Country of issue： United States (USA)  Language： ENGLISH (EN)

Abstract/Point： Exosomes are extracellular nan...

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Thesaurus term： tumor cell ,  aspect ratio ,  substrate(plate) ,  early diagnosis ,  atomic force microscope ,  *machine learning ,  principal component analysis ,  *forecast ,  higher dimension ,  support vector machine ,  *learning
Semi thesaurus term： 予測精度 ,  固定化 ,  ナノベシクル ,  *エキソソーム , 【Automatic Indexing@JST】

JST classification (1)： Structure and morphology of polymer solids  (CG02022M)

Terms in the title (7)： 機械学習 ,  解析 ,  固体表面 ,  形態学的特徴 ,  エキソソーム ,  宿主細胞 ,  予測