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 conferencePaper

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 languageEnglish
Publication statusPublished - Jan 1 2018
Externally publishedYes
Event8th 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 2018Nov 6 2018

Conference

Conference8th International Symposium on Computational Intelligence and Industrial Applications and 12th China-Japan International Workshop on Information Technology and Control Applications, ISCIIA and ITCA 2018
CountryChina
CityTengzhou, Shandong
Period11/2/1811/6/18

Keywords

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

ASJC Scopus subject areas

  • Industrial and Manufacturing Engineering
  • Artificial Intelligence

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  • 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.