TY - JOUR
T1 - Conditional generative adversarial networks to model iPSC-derived cancer stem cells
AU - Aida, Saori
AU - Kameda, Hiroyuki
AU - Nishisako, Sakae
AU - Kasai, Tomonari
AU - Sato, Atsushi
AU - Sugiyama, Tomoyasu
N1 - Funding Information:
This research is supported by Tokyo University of Technology with two grants, that is, the Advanced AI Research Grant of Bionics AI research (Professor Tomoyasu Sugiyama), and that of Computer Science AI research (Associate Professor Shino Iwashita).
PY - 2020
Y1 - 2020
N2 - The 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.
AB - The 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.
KW - Artificial intelligence
KW - Conditional generative adversarial networks
KW - Drug discovery
KW - IPSC-derived cancer stem cells
KW - Pix2pix
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UR - http://www.scopus.com/inward/citedby.url?scp=85078466622&partnerID=8YFLogxK
U2 - 10.20965/jaciii.2020.p0134
DO - 10.20965/jaciii.2020.p0134
M3 - Article
AN - SCOPUS:85078466622
SN - 1343-0130
VL - 24
SP - 134
EP - 141
JO - Journal of Advanced Computational Intelligence and Intelligent Informatics
JF - Journal of Advanced Computational Intelligence and Intelligent Informatics
IS - 1
ER -
