サトウ　アツシ   佐藤　淳    所属   応用生物学部 応用生物学科    職種   教授
言語種別	英語
発行・発表の年月	2020/01
形態種別	学術論文
査読	査読あり
標題	Conditional Generative Adversarial Networks to Model iPSC-Derived Cancer Stem Cells
執筆形態	共著
掲載誌名	Journal of Advanced Computational Intelligence and Intelligent Informatics
掲載区分	国外
巻・号・頁	24(1),pp.134-141
総ページ数	8
著者・共著者	Saori Aida, Hiroyuki Kameda, Sakae Nishisako, Tomonari Kasai, Atsushi Sato, and Tomoyasu Sugiyama
概要	he realization of effective and low-cost drug discovery is imperative to enable people to easily purchase and use medicines when necessary. This paper reports a smart system for detecting iPSC-derived cancer stem cells by using conditional generative adversarial networks. This system with artificial intelligence (AI) accepts a normal image from a microscope and transforms it into a corresponding fluorescent-marked fake image. The AI system learns 10,221 sets of paired pictures as input. Consequently, the system’s performance shows that the correlation between true fluorescent-marked images and fake fluorescent-marked images is at most 0.80. This suggests the fundamental validity and feasibility of our proposed system. Moreover, this research opens a new way for AI-based drug discovery in the process of iPSC-derived cancer stem cell detection.
外部リンクURL	https://www.jstage.jst.go.jp/article/jaciii/24/1/24_134/_article/-char/ja