Articles Citing

Machine-Learning-Inferred and Energy-Landscape-Guided Analyses Reveal Kinetic Determinants of CRISPR/Cas9 Gene Editing

Lei Jin and Risi Liyanage

Dongsheng Duan

Shi-Jie Chen^*

Department of Physics and Astronomy, University of Missouri, Columbia, Missouri 65211, USA

Department of Molecular Microbiology and Immunology and Department of Neurology, School of Medicine, University of Missouri, Columbia, Missouri 65211, USA; Department of Biomedical Sciences, College of Veterinary Medicine, University of Missouri, Columbia, Missouri 65211, USA; and Department of Bioengineering, University of Missouri, Columbia, Missouri 65211, USA

Department of Physics and Astronomy and Department of Biochemistry, Institute of Data Sciences and Informatics, University of Missouri, Columbia, Missouri 65211, USA

^*Contact author: chenshi@missouri.edu

Machine-learning in optimization of CRISPR technology

Risi Liyanage, Lei Jin, and Shi-Jie Chen

Mach. Learn.: Health 2 021001 (2026)

Advances in AI-Guided CRISPR-Cas9 Engineering Strategies for Microbial Biotechnology

Javier Alejandro Delgado-Nungaray, Dulce Alitzel Pérez-Ponce, Luis Joel Figueroa-Yáñez, Eire Reynaga-Delgado, Mario Alberto García-Ramírez, and Orfil Gonzalez-Reynoso

JGBG 1 10 (2026)

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Articles Citing

Machine-Learning-Inferred and Energy-Landscape-Guided Analyses Reveal Kinetic Determinants of CRISPR/Cas9 Gene Editing

Machine-learning in optimization of CRISPR technology

Advances in AI-Guided CRISPR-Cas9 Engineering Strategies for Microbial Biotechnology

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