Training the Next Generation: A Generative AI-enhanced Framework for Teaching Bioinformatics to Life Science Students with Relevance to Cardiovascular Innovation

Main Article Content

Camellia Moses Okpodu*
Samelia Okpodu-Pyuzza

Abstract

Abstract


Bioinformatics is increasingly essential for life science students preparing for careers in biomedical research, biotechnology, and related fields. However, many undergraduate programs lack accessible entry points for teaching computational biology. This short communication presents a faculty-led instructional framework that integrates Python and generative AI tools to introduce bioinformatics in a life sciences curriculum. Designed for instructors without formal programming backgrounds, this approach emphasizes hands-on learning and interdisciplinary relevance. Our experience shows that generative AI can help reduce technical barriers for life science faculty and support scalable instruction in computational methods and data-centered biological exploration.

Downloads

Download data is not yet available.

Article Details

Camellia Moses Okpodu*, & Okpodu-Pyuzza, S. (2025). Training the Next Generation: A Generative AI-enhanced Framework for Teaching Bioinformatics to Life Science Students with Relevance to Cardiovascular Innovation. Global Journal of Medical and Clinical Case Reports, 140–142. https://doi.org/10.17352/2455-5282.000214
Short Communications

Copyright (c) 2025 Okpodu CM, et al.

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Licensing and protecting the author rights is the central aim and core of the publishing business. Peertechz dedicates itself in making it easier for people to share and build upon the work of others while maintaining consistency with the rules of copyright. Peertechz licensing terms are formulated to facilitate reuse of the manuscripts published in journals to take maximum advantage of Open Access publication and for the purpose of disseminating knowledge.

We support 'libre' open access, which defines Open Access in true terms as free of charge online access along with usage rights. The usage rights are granted through the use of specific Creative Commons license.

Peertechz accomplice with- [CC BY 4.0]

Explanation

'CC' stands for Creative Commons license. 'BY' symbolizes that users have provided attribution to the creator that the published manuscripts can be used or shared. This license allows for redistribution, commercial and non-commercial, as long as it is passed along unchanged and in whole, with credit to the author.

Please take in notification that Creative Commons user licenses are non-revocable. We recommend authors to check if their funding body requires a specific license.

With this license, the authors are allowed that after publishing with Peertechz, they can share their research by posting a free draft copy of their article to any repository or website.
'CC BY' license observance:

License Name

Permission to read and download

Permission to display in a repository

Permission to translate

Commercial uses of manuscript

CC BY 4.0

Yes

Yes

Yes

Yes

The authors please note that Creative Commons license is focused on making creative works available for discovery and reuse. Creative Commons licenses provide an alternative to standard copyrights, allowing authors to specify ways that their works can be used without having to grant permission for each individual request. Others who want to reserve all of their rights under copyright law should not use CC licenses.

Zawacki-Richter O, Marín VI, Bond M, Gouverneur F. Systematic review of research on artificial intelligence applications in higher education: Where are the educators? Int J Educ Technol High Educ. 2019;16(1):39. Available from: https://doi.org/10.1186/s41239-019-0171-0

Alpaydin E, Aydin M. Biological computation and computational biology: Survey, challenges, and perspectives. Artif Intell Rev. 2020;53:1–28. Available from: https://doi.org/10.1007/s10462-020-09951-1

Viet NK. The use of generative AI tools in higher education: Ethical and pedagogical principles. J Acad Ethics. 2025. Available from: https://doi.org/10.1007/s10805-025-09607-1

Mehrgardt P, Khushi M, Poon S, Withana A. Pulse Transit Time PPG Dataset (version 1.1.0). PhysioNet. 2022. RRID:SCR_007345. Available from: https://doi.org/10.13026/jpan-6n92

Goldberger A, Amaral L, Glass L, Hausdorff J, Ivanov PC, Mark R, et al. PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals. Circulation [Online]. 2000;101(23):e215–e220. Available from: https://doi.org/10.1161/01.cir.101.23.e215

Lin M, Guo J, Gu Z, Tang W, Tao H, You S, et al. Machine learning and multi-omics integration: Advancing cardiovascular translational research and clinical practice. J Transl Med. 2025;23:Article 388. Available from: https://doi.org/10.1186/s12967-025-06425-2

Yunusa HH. The impact of physical activity on oxygen saturation levels. Int J Physiol Health Phys Educ. 2023;5(1):21–3. Available from: https://doi.org/10.33545/26647265.2023.v5.i1a.53

Khera R, Topol EJ. The potential of AI in cardiovascular care: Focus of review. J Am Coll Cardiol (JACC) – State-of-the-Art Review. 2024 Jun 24. Available from: https://www.acc.org/Latest-in-Cardiology/Articles/2024/06/24/16/51/The-Potential-of-AI-in-Cardiovascular-Care-Focus-of-Review