Programme Parameters
Graduate / Intermediate
3 Days (4.5 Hrs)
Online (e-LMS)
• A basic understanding of the subject area and fundamental programming or scientific concepts.
• A laptop or desktop with a stable internet connection.
• Willingness to complete assignments and the capstone project.
About This Course
This 3‑day hands‑on course introduces participants to the COMET Framework, a research‑inspired approach for multimodal biomedical AI. Every participant receives a verified e-Certificate and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
This 3‑day hands‑on course introduces participants to the COMET Framework, a research‑inspired approach for multimodal biomedical AI.
📋 Course Objectives
1. Translate biotechnology theory into practical, reproducible analysis.
2. Assemble a documented case study that evidences your applied capability.
👥 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
🚀 Key Learning Outcomes
• Tangible, reproducible biotechnology work to show supervisors or employers.
• 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
Day 1 – Clinical‑Omics Multimodal AI & COMET Foundations
Explore global trends in clinical AI and precision medicine • Identify clinical, omics, biomarker, and EHR data modalities • Create a synthetic multimodal dataset in Google Colab
Day 2 – Transfer Learning, Foundation Models & Transformer Fusion
Apply transfer learning to small biomedical cohorts • Leverage pretrained transformer models for EHR text • Build a multimodal disease‑risk prediction pipeline in Colab
Day 3 – Benchmarking, Explainability & Responsible AI
Compare unimodal, bimodal, and multimodal model performance • Evaluate models with ROC‑AUC, F1‑score, and confusion matrix • Interpret feature importance and ensure responsible AI practices
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | Google Colab |
| Covered Tool / Platform | Python |
| Covered Tool / Platform | PyTorch |
| Covered Tool / Platform | Hugging Face Transformers |
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