Selected for the Korea Research Foundation's Master's Student Research Fellowship Program / Seung-Hee Han (Master of Science in Biofermentation and Convergence, Graduate School of General Studies, 24)
- 24.10.04 / 이정민
Seunghee Han, a master's student in the Biofermentation and Convergence Major at Kookmin University's Graduate School of General Studies, was recently selected as a finalist for the National Research Foundation of Korea's Master's Student Research Grant Program.
Seunghee Han has been selected for the 2024 Research Grants for Graduate Students of the National Research Foundation of Korea (NRF) for the project titled 'Development of machine learning-based enzyme engineering workflow'. The research aims to develop a machine learning model that designs mutant enzymes with desired reaction properties using lab-scale experimental data as training data. This research will enable enzyme engineering based on machine learning to develop new mutant enzymes in a short period of time, and since these machine learning models can be applied to enzymes for which high-efficiency enzyme screening methods such as color reaction do not exist, it is expected that industrial enzyme development and synthetic pathway development in synthetic biology can be performed in less time and cost.
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Selected for the Korea Research Foundation's Master's Student Research Fellowship Program / Seung-Hee Han (Master of Science in Biofermentation and Convergence, Graduate School of General Studies, 24) |
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2024-10-04
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Seunghee Han, a master's student in the Biofermentation and Convergence Major at Kookmin University's Graduate School of General Studies, was recently selected as a finalist for the National Research Foundation of Korea's Master's Student Research Grant Program.
Seunghee Han has been selected for the 2024 Research Grants for Graduate Students of the National Research Foundation of Korea (NRF) for the project titled 'Development of machine learning-based enzyme engineering workflow'. The research aims to develop a machine learning model that designs mutant enzymes with desired reaction properties using lab-scale experimental data as training data. This research will enable enzyme engineering based on machine learning to develop new mutant enzymes in a short period of time, and since these machine learning models can be applied to enzymes for which high-efficiency enzyme screening methods such as color reaction do not exist, it is expected that industrial enzyme development and synthetic pathway development in synthetic biology can be performed in less time and cost.
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