Learn Essential Elements of Building an AI Solution for Life Sciences – A Case Study Based Approach
At the core of an Artificial Intelligence solution, there are machine learning models. The rise of foundation models—large-scale AI systems trained on vast datasets—has revolutionized the way we approach complex problems in life sciences. This comprehensive eLearning tutorial offers a deep dive into the fundamentals of foundation models, tailored specifically for researchers, students, and professionals in biology, medicine, and bioinformatics.
What you’ll learn
- Basics of Foundation Models.
- AI Models for Life Sciences Domain.
- Benefits and Limitations of Foundation Models.
- Accessing Foundation Models.
Course Content
- Introduction –> 6 lectures • 21min.
- AI Models in Life Sciences –> 3 lectures • 12min.
- AI Models on Cloud Platforms –> 4 lectures • 20min.
- Implementation – Approaches to Use AI Models –> 4 lectures • 9min.
- Implementation – Deployment Approaches for AI Foundation Models –> 4 lectures • 12min.
- Implementation – Critical Considerations While Using AI Models –> 4 lectures • 14min.
- Conclusion –> 1 lecture • 1min.
Requirements
At the core of an Artificial Intelligence solution, there are machine learning models. The rise of foundation models—large-scale AI systems trained on vast datasets—has revolutionized the way we approach complex problems in life sciences. This comprehensive eLearning tutorial offers a deep dive into the fundamentals of foundation models, tailored specifically for researchers, students, and professionals in biology, medicine, and bioinformatics.
You’ll begin by exploring the core concepts: what foundation models are, the different types that exist, and where to access them. Through engaging modules, you’ll uncover the transformative benefits these models bring to life sciences, from accelerating research to enabling new forms of discovery.
The course then zooms in on cutting-edge applications, including how foundation models are reshaping drug discovery pipelines and how GPT-like architectures are being adapted for biological data and clinical insights. You’ll learn how these models can predict molecular interactions, generate hypotheses, and assist in personalized medicine.
To ensure practical understanding, the tutorial also covers how to evaluate foundation models for life science tasks—highlighting key metrics, validation strategies, and ethical considerations. Finally, it addresses the limitations and challenges, such as data bias, interpretability, and regulatory hurdles.
By the end of this course, you’ll be equipped with the knowledge to critically assess and apply foundation models in your own scientific endeavours.
Note: This training is for educational purpose only and not an advise on how to implement or recommendation for any implementation real-time.