International Workshop on Foundation Models for Life Sciences
Reimagining Biology with Foundation Models and Next-Generation AI
About This Course
Foundation models, originally popularized in natural language processing and computer vision, are now reshaping the life sciences by learning from massive biological datasets such as DNA, RNA, proteins, biomedical literature, pathology images, and multi-omics data. These models can be adapted to many downstream tasks with minimal retraining, making them powerful tools for protein structure prediction, sequence design, disease modeling, biomarker discovery, and drug development.
Aim
This workshop aims to introduce participants to the rapidly evolving field of foundation models in life sciences, covering how large pre-trained AI models are transforming biological research. It focuses on applying foundation models to sequences, structures, omics data, imaging, and biomedical text.
Workshop Objectives
- Understand the concept and architecture of foundation models in life sciences.
- Explore applications in DNA, RNA, protein, imaging, and biomedical text analysis.
- Learn workflows for prompting, fine-tuning, and evaluating biological foundation models.
- Study multimodal integration for prediction and discovery tasks.
- Discuss ethical, translational, and industrial adoption of foundation models in biology.
Workshop Structure
Day 1: Foundations of AI and Foundation Models in Life Sciences
- What foundation models are?
- Transformers, embeddings, tokenization
- Why they matter in biology and medicine
- Sequences, structures, omics, imaging, clinical text
- Data preprocessing and representation
- Challenges: noise, scale, annotation scarcity
- Hands-on: Exploring biological datasets
- Generating embeddings from biological sequences/text
- Tools: Google Colab/Python/Jupyter Notebook/ Hugging Face Transformers/PyTorch/Pandas/ NumPy
Day 2: Applying Foundation Models to Life Science Problems
- Protein language models
- DNA/RNA sequence modeling
- Biomedical LLM applications
- Prompt engineering for biology tasks
- Transfer learning and parameter-efficient tuning
- Evaluation strategies
- Hands-on: Running inference with pretrained bio-models
- Fine-tuning a small model for classification or annotation
- Tools: Hugging Face/ ESM / ProtTrans style models/ BioPython/ Scikit-learn/ Google Colab
Day 3: Building a Mini Life Sciences AI Workflow
- Problem framing, Data pipeline design
- Model selection and output interpretation
- Drug discovery, Biomarker detection
- clinical and regulatory concerns
- Hands-on: Build a mini pipeline for sequence/text classification or biological annotation, Present results and limitations
- Tools: Google Colab/ Python/ Streamlit or Gradio/ Matplotlib / Seaborn/ GitHub
Who Should Enrol?
- Undergraduate/postgraduate degree in Bioinformatics, Biotechnology, Computational Biology, Genomics, Biomedical Sciences, Computer Science, or related fields.
- Professionals working in biotech, pharma, healthcare AI, genomics, or biomedical research sectors.
- Data scientists and AI/ML engineers interested in applying foundation models to biological and medical datasets.
- Individuals with a keen interest in the convergence of AI, biology, and life science innovation.
Important Dates
Registration Ends
04/16/2026
IST 7:00 PM IST
Workshop Dates
04/16/2026 – 04/18/2026
IST 8:00 PM IST
Workshop Outcomes
Participants will be able to:
- Explain how foundation models are built and applied in life sciences.
- Identify suitable model types for sequence, structure, image, and text tasks.
- Understand workflows for adaptation, prompting, and downstream prediction.
- Interpret outputs from biological AI models in a research context.
- Propose foundation-model-driven solutions for genomics, medicine, and biotech problems.
Fee Structure
Student Fee
₹2499 | $75
Ph.D. Scholar / Researcher Fee
₹3499 | $85
Academician / Faculty Fee
₹3499 | $95
Industry Professional Fee
₹4499 | $105
What You’ll Gain
- Live & recorded sessions
- e-Certificate upon completion
- Post-workshop query support
- Hands-on learning experience
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