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Citation: Ying Zhang, Fang Ge, Zhimin Luo. AI-Driven Biochemical Teaching Research: Predicting the Functional Effects of Gene Mutations[J]. University Chemistry, ;2025, 40(3): 277-284. doi: 10.12461/PKU.DXHX202412104 shu

AI-Driven Biochemical Teaching Research: Predicting the Functional Effects of Gene Mutations

Jiangsu Key Laboratory of Smart Biomaterials and Theranostic Technology, State Key Laboratory of Flexible Electronics (LoFE), College of Electronic and Optical Engineering & College of Flexible Electronics (Future Technology), School of Chemistry and Life Sciences, Nanjing University of Posts and Telecommunications, Nanjing 210023, China

Corresponding author: Fang Ge, Zhimin Luo,
Received Date: 13 December 2024
Revised Date: 24 February 2025

Guided by the principle of “integrating science and education, collaboratively cultivating talent”, this paper explores the integration of Artificial Intelligence (AI) with biochemistry teaching and research. It examines the application of AI technology in the reform of biochemistry education, specifically through the development of a case study in biomedical engineering that predicts the functional effects of gene mutations using AI. The paper discusses the design and implementation of this AI-driven teaching case, focusing on the case’s background, curriculum design, teaching strategies, and evaluation of its impact. This approach aims to cultivate interdisciplinary thinking in students, enhancing their ability to integrate knowledge across fields.
- Biochemistry,
- Case teaching,
- Gene mutation,
- Functional effect,
- Artificial intelligence
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