[P002] Poster

1:00 PM - 1:50 PM JST(4:00 AM - 4:50 AM UTC)

[P01-032] Leveraging high-throughput sequencing and deep learning to predict high lipid-producing variants in Yarrowia lipolytica

○Jaehoon Kim¹, Min Kyeong Kim², Sungmin Hwang³, Yongjae Lee⁴, Jiwon Kim¹, Ji Hyun Jung¹, Kyung-Jin Kim⁵, Hyeoncheol Francis Son⁶, Sun-Mi Lee⁴, Daechan Park^1,7 (1. Department of Molecular Science and Technology, Ajou Univ., Suwon 16499, Republic of Korea (Korea), 2. Clean Energy Research Center, Korea Institute of Science and Technology (KIST), Seoul, 02792, Republic of Korea (Korea), 3. Division of Convergence on Marine Science, Korea Maritime & Ocean Univ., Busan 49112, Republic of Korea (Korea), 4. Department of Environmental Science and Ecological Engineering, Korea Univ., Seoul 02841, Republic of Korea (Korea), 5. KNU Institute for Microorganisms, Kyungpook National Univ., Daegu, Republic of Korea (Korea), 6. School of Biological Sciences and Technology, Chonnam National Univ., Gwangju 61186, Republic of Korea (Korea), 7. Advanced College of Bio-Convergence Engineering, Ajou Univ., Suwon 16499, Republic of Korea (Korea))

Keywords:

Enzyme engineering,Directed evolution,High-throughput sequencing,Deep learning,Synthetic biology

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