Application of conditional generative adversarial nets to iPSC-derived cancer stem cell modeling

Hiroyuki Kameda; Saori Aida; Sakae Nishisako; Tomonari Kasai; Atsushi Sato; Tomoyasu Sugiyama

Application of conditional generative adversarial nets to iPSC-derived cancer stem cell modeling

Hiroyuki Kameda, Saori Aida, Sakae Nishisako, Tomonari Kasai, Atsushi Sato, Tomoyasu Sugiyama

Research output: Contribution to conference › Paper › peer-review

Abstract

This paper proposes a new artificial intelligence application to discover new drugs. CGAN (Conditional Generative Adversarial Nets), which is one of Deep Learning algorithms, is applied to detect iPSC-derived cancer stem cells. Fundamental validity of this method was showed by experiments.

Original language	English
Publication status	Published - 2018
Externally published	Yes
Event	8th International Symposium on Computational Intelligence and Industrial Applications and 12th China-Japan International Workshop on Information Technology and Control Applications, ISCIIA and ITCA 2018 - Tengzhou, Shandong, China Duration: Nov 2 2018 → Nov 6 2018

Conference

Conference	8th International Symposium on Computational Intelligence and Industrial Applications and 12th China-Japan International Workshop on Information Technology and Control Applications, ISCIIA and ITCA 2018
Country/Territory	China
City	Tengzhou, Shandong
Period	11/2/18 → 11/6/18

Keywords

Conditional Generative Adversarial Nets
IPSC-derived Cancer Stem Cells Modeling
New Drug Discovery

ASJC Scopus subject areas

Industrial and Manufacturing Engineering
Artificial Intelligence

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Cite this

Kameda, H., Aida, S., Nishisako, S., Kasai, T., Sato, A., & Sugiyama, T. (2018). Application of conditional generative adversarial nets to iPSC-derived cancer stem cell modeling. Paper presented at 8th International Symposium on Computational Intelligence and Industrial Applications and 12th China-Japan International Workshop on Information Technology and Control Applications, ISCIIA and ITCA 2018, Tengzhou, Shandong, China.

Application of conditional generative adversarial nets to iPSC-derived cancer stem cell modeling. / Kameda, Hiroyuki; Aida, Saori; Nishisako, Sakae et al.
2018. Paper presented at 8th International Symposium on Computational Intelligence and Industrial Applications and 12th China-Japan International Workshop on Information Technology and Control Applications, ISCIIA and ITCA 2018, Tengzhou, Shandong, China.

Research output: Contribution to conference › Paper › peer-review

Kameda, H, Aida, S, Nishisako, S, Kasai, T, Sato, A & Sugiyama, T 2018, 'Application of conditional generative adversarial nets to iPSC-derived cancer stem cell modeling', Paper presented at 8th International Symposium on Computational Intelligence and Industrial Applications and 12th China-Japan International Workshop on Information Technology and Control Applications, ISCIIA and ITCA 2018, Tengzhou, Shandong, China, 11/2/18 - 11/6/18.

Kameda H, Aida S, Nishisako S, Kasai T, Sato A, Sugiyama T. Application of conditional generative adversarial nets to iPSC-derived cancer stem cell modeling. 2018. Paper presented at 8th International Symposium on Computational Intelligence and Industrial Applications and 12th China-Japan International Workshop on Information Technology and Control Applications, ISCIIA and ITCA 2018, Tengzhou, Shandong, China.

Kameda, Hiroyuki ; Aida, Saori ; Nishisako, Sakae et al. / Application of conditional generative adversarial nets to iPSC-derived cancer stem cell modeling. Paper presented at 8th International Symposium on Computational Intelligence and Industrial Applications and 12th China-Japan International Workshop on Information Technology and Control Applications, ISCIIA and ITCA 2018, Tengzhou, Shandong, China.

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title = "Application of conditional generative adversarial nets to iPSC-derived cancer stem cell modeling",

abstract = "This paper proposes a new artificial intelligence application to discover new drugs. CGAN (Conditional Generative Adversarial Nets), which is one of Deep Learning algorithms, is applied to detect iPSC-derived cancer stem cells. Fundamental validity of this method was showed by experiments.",

keywords = "Conditional Generative Adversarial Nets, IPSC-derived Cancer Stem Cells Modeling, New Drug Discovery",

author = "Hiroyuki Kameda and Saori Aida and Sakae Nishisako and Tomonari Kasai and Atsushi Sato and Tomoyasu Sugiyama",

note = "Funding Information: This research is supported by Tokyo University of Technology with two grants, i.e., the Advanced AI Research Grant of Bionics AI research (Prof. Tomoyasu Sugiyama), and that of Computer Science AI research (Associate Prof. Shino Iwashita). Publisher Copyright: {\textcopyright} ISCIIA and ITCA 2018 - 8th International Symposium on Computational Intelligence and Industrial Applications and 12th China-Japan International Workshop on Information Technology and Control Application.; 8th International Symposium on Computational Intelligence and Industrial Applications and 12th China-Japan International Workshop on Information Technology and Control Applications, ISCIIA and ITCA 2018 ; Conference date: 02-11-2018 Through 06-11-2018",

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T1 - Application of conditional generative adversarial nets to iPSC-derived cancer stem cell modeling

AU - Kameda, Hiroyuki

AU - Aida, Saori

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, i.e., the Advanced AI Research Grant of Bionics AI research (Prof. Tomoyasu Sugiyama), and that of Computer Science AI research (Associate Prof. Shino Iwashita). Publisher Copyright: © ISCIIA and ITCA 2018 - 8th International Symposium on Computational Intelligence and Industrial Applications and 12th China-Japan International Workshop on Information Technology and Control Application.

PY - 2018

Y1 - 2018

N2 - This paper proposes a new artificial intelligence application to discover new drugs. CGAN (Conditional Generative Adversarial Nets), which is one of Deep Learning algorithms, is applied to detect iPSC-derived cancer stem cells. Fundamental validity of this method was showed by experiments.

AB - This paper proposes a new artificial intelligence application to discover new drugs. CGAN (Conditional Generative Adversarial Nets), which is one of Deep Learning algorithms, is applied to detect iPSC-derived cancer stem cells. Fundamental validity of this method was showed by experiments.

KW - Conditional Generative Adversarial Nets

KW - IPSC-derived Cancer Stem Cells Modeling

KW - New Drug Discovery

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M3 - Paper

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T2 - 8th International Symposium on Computational Intelligence and Industrial Applications and 12th China-Japan International Workshop on Information Technology and Control Applications, ISCIIA and ITCA 2018

Y2 - 2 November 2018 through 6 November 2018

ER -

Application of conditional generative adversarial nets to iPSC-derived cancer stem cell modeling

Abstract

Conference

Keywords

ASJC Scopus subject areas

UN SDGs

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