Integrating Large Language Models in Bioinformatics Education for Medical Students: Opportunities and Challenges
Author:
Funder
National Natural Science Foundation of China
Natural Science Foundation of Sichuan Province
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s10439-024-03554-5.pdf
Reference11 articles.
1. A. Beg and R. Parveen: Role of bioinformatics in cancer research and drug development. In Translational Bioinformatics in Healthcare and Medicine. Elsevier; 2021: 141–148.
2. Attwood, T. K., S. Blackford, M. D. Brazas, A. Davies, and M. V. Schneider. A global perspective on evolving bioinformatics and data science training needs. Brief. Bioinform. 20(2):398–404, 2019.
3. Clusmann, J., F. R. Kolbinger, H. S. Muti, Z. I. Carrero, J.-N. Eckardt, N. G. Laleh, C. M. L. Löffler, S.-C. Schwarzkopf, M. Unger, and G. P. Veldhuizen. The future landscape of large language models in medicine. Commun. Med. 3(1):141, 2023.
4. Piccolo, S. R., P. Denny, A. Luxton-Reilly, S. H. Payne, and P. G. Ridge. Evaluating a large language model’s ability to solve programming exercises from an introductory bioinformatics course. PLoS Comput. Biol. 19(9):e1011511, 2023.
5. Chatterjee, A., A. Ahn, E. J. Rodger, P. A. Stockwell, and M. R. Eccles. A guide for designing and analyzing RNA-Seq data. In: Gene Expression Analysis. Methods in Molecular Biology, edited by N. Raghavachari, and N. Garcia-Reyero. New York: Humana Press, 2018, pp. 35–80.
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