Programme Parameters
Foundation
3 Days (4.5 Hrs)
Online (e-LMS)
• No prior experience required — basic computer literacy is sufficient.
• A laptop or desktop with a stable internet connection.
• Willingness to complete assignments and the capstone project.
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. Across 4 Weeks, you will work hands-on with biomedical literature and pathology images, then consolidate everything in a capstone project. Every participant receives a verified e-Certificate and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
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.
📋 Course Objectives
1. Gain working command of biomedical literature.
2. Develop hands-on skill in pathology images.
3. Translate biotechnology theory into practical, reproducible analysis.
4. Produce a reproducible, portfolio-ready project you can cite in a thesis, paper, or job application.
👥 Who Should Enroll?
• Master's and senior undergraduate students specializing in biotechnology
• R&D engineers and working professionals applying biotechnology in industry
• Academics and educators building research or teaching capacity in biotechnology
• Data and computational scientists moving into biomedical literature
🚀 Key Learning Outcomes
• Confidence to reason about biomedical literature in real projects.
• Confidence to apply pathology images in real projects.
• A portfolio-grade biotechnology deliverable you can defend and extend.
• A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
💎 What You'll Gain
Live & Recorded Sessions
Lifetime access to class recordingse-Certificate on Completion
Cryptographically verified credentialPost-Programme Support
Direct access to mentors & councilHands-On Experience
Notebooks, real-world code & datasetsCurriculum Outline
What Makes a Model a Foundation Model
• Self-supervised pretraining at scale and transfer to many downstream tasks
• Why biology suited this approach: abundant sequence, scarce labels
• Scaling behaviour and the point where more data stops helping
Protein and Nucleic Acid Models
• Protein language models such as ESM and what their embeddings capture
• Genomic models and the tokenisation problem specific to DNA
• Structure prediction as the most convincing demonstration to date
Images, Text and Cells
• Pathology and microscopy foundation models and their annotation dependence
• Biomedical literature models and single-cell models such as Geneformer
• Multi-omics and the difficulty of aligning modalities from different assays
Judging a Model Honestly
• Benchmarks, leakage between pretraining data and test sets
• Zero-shot and few-shot claims and how often a simple baseline matches them
• Reproducibility, licensing and weights that are announced but not released
Applying Them to Your Own Work
• Embeddings as features against fine-tuning for a specific task
• Compute requirements and what is achievable on modest hardware
• Where these models fail biologically and the experimental check that catches it
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | RStudio |
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