발간년도 : [2021]
| 논문정보 |
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| 논문명(한글) |
[Vol.16, No.4] A Design and Performance Evaluation of Automatic Cell Image Generating System Using COVID-19 Style Transformation Model |
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| 논문투고자 |
Ha Rim Lee, Yong Do Her |
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| 논문내용 |
Recently, several types of vaccines for COVID-19 have been developed and are being used worldwide. However, there is still a lot of time is needed for the preventive effect by the vaccine to come out. Therefore, it can be said that the development of an infectious disease treatment that can be used when contracting COVID-19 is a very important issue. However, developing a treatment for COVID-19 requires more time than developing a vaccine. There are many reasons, but among the many drug candidates that can be used in remedy, until a good drug candidate is found that can inhibit or overcome COVID-19, experiments on cell culture and chemical reactions must continue and iteratively, and the results of the chemical reaction must be evaluated. This is because researchers have to check and make judgments one by one. Therefore, in this paper, in order to shorten the clinical trial time required for the development of a remedy agent for the COVID-19, we designed a system that can automatically transform and show the chemical reaction result when a candidate drugs are administered to the COVID-19 virus in a short time and the performance was evaluated. A style transformation model based on StarGAN-v2 designed in this paper supports conversion between multiple styles. In addition, using the automated style transformation model, a candidate drug or compound is directly administered to a specific virus cell and the results can be simulated quickly without waiting for the results of a clinical trial waiting for the results. Therefore, if the style transformation model designed in this paper is used as a simulator for administration results in the clinical trial stage of new drug development, it will be able to contribute to shortening the clinical trial procedure and time. |
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