About Workshop
This 3-day hands-on workshop introduces participants to the COMET Framework, a research-inspired approach for multimodal biomedical AI. The workshop focuses on integrating clinical data, omics features, biomarkers, and EHR-style text using transfer learning, transformer-based representations, and multimodal fusion techniques.
Designed for researchers, academicians, and industry professionals, this workshop helps participants understand how modern AI can support data-scarce biomedical research, disease-risk prediction, precision medicine, patient stratification, and biomarker discovery. Participants will work through Google Colab-based hands-on activities to build a simplified COMET-inspired clinical-omics AI pipeline.
Aim
The aim of this workshop is to provide participants with practical and research-oriented knowledge of clinical-omics multimodal AI, transfer learning, and transformer-based data fusion for biomedical research.
The workshop aims to help participants understand how clinical records, omics data, and EHR-style text can be combined to develop AI models that support biomedical prediction, research replication, and precision medicine applications.What Participants Will Learn
By attending this workshop, participants will learn to:
- Understand the role of multimodal AI in biomedical and clinical research
- Explore the COMET Framework for clinical, omics, and EHR data integration
- Understand data scarcity challenges in biomedical AI research
- Learn transfer learning concepts for small biomedical datasets
- Understand transformer-based representations for EHR-style text
- Explore early fusion, late fusion, and hybrid fusion strategies
- Build a simplified clinical-omics multimodal AI pipeline in Google Colab
- Compare unimodal and multimodal model performance
- Apply basic model evaluation and explainability techniques
- Connect the workflow with research replication, precision medicine, and biomarker discovery
Structure
📅 Day 1: Clinical-Omics Multimodal AI and COMET Research Foundations
- Global trends in clinical AI, omics integration, and precision medicine
- Overview of clinical data, omics data, biomarkers, and EHR records
- Introduction to the COMET Framework and research-replication approach
- Data scarcity challenges in biomedical and omics studies
- Multimodal fusion strategies: early fusion, late fusion, and hybrid fusion
- Research use cases: disease prediction, patient stratification, and biomarker discovery
🛠️ Hands-on:
- Hands-on Activity: Create a synthetic clinical + omics + EHR-style dataset in Google Colab and prepare it for multimodal analysis.
📅 Day 2: Transfer Learning, Foundation Models, and Transformer-Based Fusion
- Transfer learning for small biomedical datasets
- Biomedical foundation models and pretrained clinical representations
- Transformer models for EHR text and clinical notes
- Feature engineering for clinical and omics data
- Combining tabular features with transformer-based text embeddings
- Handling missing modalities and small-cohort limitations
- Building a COMET-inspired multimodal prediction pipeline
🛠️ Hands-on:
- Hands-on Activity: Build a multimodal disease-risk prediction model using clinical features, omics features, and EHR-style text embeddings in Google Colab.
📅 Day 3: Benchmarking, Explainability, Responsible AI, and Research Replication
- Comparing unimodal, bimodal, and multimodal model performance
- Model evaluation using accuracy, F1-score, ROC-AUC, and confusion matrix
- Benchmarking and reproducibility in biomedical AI research
- Explainability for clinical and omics predictions using feature importance
- Responsible AI: privacy, bias, fairness, and clinical trust
- Translational applications in precision medicine, pharma, and healthcare analytics
- Preparing a mini research-style replication summary
🛠️ Hands-on:
- Hands-on Activity: Evaluate the COMET-inspired model, interpret key features, visualize results, and prepare a mini research-style report in Google Colab.
Important Dates
Registration Ends
4.30 pm
Workshop Dates
2026-06-15
5.30 PM
5.30 PM
What You Will Gain
Outcomes
After completing this workshop, participants will be able to:
- Explain how clinical data, omics features, biomarkers, and EHR-style text can be integrated using multimodal AI
- Understand the importance of transfer learning in data-scarce biomedical research
- Prepare a basic clinical + omics + EHR-style dataset for AI modeling
- Build a simplified COMET-inspired multimodal prediction pipeline
- Use transformer-based embeddings for unstructured clinical text representation
- Evaluate model performance using accuracy, F1-score, ROC-AUC, and confusion matrix
- Interpret important clinical and omics features from model outputs
- Understand responsible AI considerations such as privacy, bias, fairness, and clinical trust
- Develop a mini research-style summary from model results and visualizations
- Apply the learning to biomedical research areas such as disease prediction, patient stratification, biomarker discovery, precision medicine, and healthcare analytics
Who Should Attend
This workshop is suitable for:
- Researchers working in biomedical science, clinical AI, bioinformatics, omics, or healthcare analytics
- Academicians and faculty members interested in AI-driven biomedical research
- PhD scholars and postgraduate learners working with healthcare, genomics, or biomedical datasets
- Industry professionals from biotechnology, pharma, healthcare AI, diagnostics, and precision medicine
- Data science and AI professionals interested in clinical and biomedical applications
- Professionals who want to understand multimodal AI, transfer learning, and EHR-based biomedical modeling