Abstract
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.
Original language | English |
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Pages (from-to) | 134-141 |
Number of pages | 8 |
Journal | Journal of Advanced Computational Intelligence and Intelligent Informatics |
Volume | 24 |
Issue number | 1 |
DOIs | |
Publication status | Published - 2020 |
Externally published | Yes |
Keywords
- Artificial intelligence
- Conditional generative adversarial networks
- Drug discovery
- IPSC-derived cancer stem cells
- Pix2pix
ASJC Scopus subject areas
- Human-Computer Interaction
- Computer Vision and Pattern Recognition
- Artificial Intelligence