Foundation (AI) Models in Life Sciences
Published 11/2025
Duration: 36m | .MP4 1920x1080 30 fps(r) | AAC, 44100 Hz, 2ch | 373.22 MB
Genre: eLearning | Language: English
Published 11/2025
Duration: 36m | .MP4 1920x1080 30 fps(r) | AAC, 44100 Hz, 2ch | 373.22 MB
Genre: eLearning | Language: English
Your AI journey to reimagine life sciences starts here
What you'll learn
- Basics of Foundation Models
- AI Models for Life Sciences Domain
- Benefits and Limitations of Foundation Models
- Accessing Foundation Models
Requirements
- Basic Understanding of AI
- Basic Understanding of Life Sciences Domain
Description
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 endeavors.
Note: This training is for educational purpose only and not an advise on how to implement or recommendation for any implementation real-time.
Who this course is for:
- Pharmaceutical Professionals
- IT Professionals
More Info