📅 Day 1: Introduction to Multi-Omics & AI Integration
Core Objective: Understand the foundations of multi-omics research, explore major omics data types, and learn how artificial intelligence supports data integration, visualization, feature selection, and biomarker discovery.
- Introduction to multi-omics and integrative biological analysis
- Types of omics: Genomics, Transcriptomics, Proteomics, and Metabolomics
- Applications of multi-omics in biomedical research and clinical settings
- Understanding data heterogeneity, dimensionality, and scaling challenges
- Strategies for integrating heterogeneous omics datasets
- Principal Component Analysis (PCA) for dimensionality reduction
- t-SNE and UMAP for multi-dimensional biological data visualization
- Feature-selection approaches using Lasso and Random Forest
- AI-assisted cancer biomarker discovery using multi-omics data
- Rare-disease biomarker discovery through AI-integrated omics analysis
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Hands-on Lab: Explore a multi-omics dataset, perform dimensionality-reduction and visualization using PCA, t-SNE, and UMAP, and apply feature-selection approaches to identify candidate biomarker features.
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Tools Covered: Python, Jupyter Notebook, PCA, t-SNE, UMAP, Lasso, Random Forest, Multi-Omics Datasets
📅 Day 2: AI Tools for Multi-Omics Analysis
Core Objective: Build practical skills in preprocessing, integrating, modelling, and evaluating multi-omics datasets using machine-learning frameworks and bioinformatics platforms.
- Handling missing biological data
- Normalization and scaling of omics datasets
- Integrating genomics, transcriptomics, proteomics, and other omics data
- Introduction to multi-omics modelling frameworks such as MOFA
- Overview of DeepMOCCA for deep-learning-based multi-omics analysis
- Setting up reproducible multi-omics workflows using Python and Jupyter Notebooks
- Using bioinformatics platforms such as Galaxy, Bioconductor, and OmicSoft
- Handling imbalanced biological datasets using SMOTE
- Undersampling and weighted-loss approaches for class imbalance
- Model evaluation using ROC-AUC, F1 Score, and related performance metrics
- Applying scikit-learn, TensorFlow, and PyTorch to multi-omics analysis
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Hands-on Lab: Preprocess and integrate multi-omics datasets, handle missing and imbalanced data, train an introductory machine-learning model, and evaluate model performance using standard classification metrics.
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Tools Covered: Python, Jupyter Notebook, MOFA, DeepMOCCA, Galaxy, Bioconductor, OmicSoft, scikit-learn, TensorFlow, PyTorch, SMOTE
📅 Day 3: Translational Applications & Biomarker Validation
Core Objective: Translate multi-omics and AI findings into clinically relevant applications through biomarker validation, drug-response prediction, clinical-trial modelling, and reproducible analysis pipelines.
- Design and implementation of biomarker-validation studies
- Regulatory frameworks for AI-enabled healthcare applications
- Introduction to FDA and EMA considerations for biomedical AI
- Ethical concerns including data privacy and informed consent
- Bias, fairness, and responsible use of AI in biomedical research
- Machine-learning models for predicting drug efficacy and treatment response
- AI-driven clinical-trial design and outcome prediction
- Experimental techniques for biomarker validation
- ELISA, PCR, and Western Blotting for biological validation
- Building a multi-omics analysis pipeline using R
- Introduction to OmicAnalyzer and MAST-based workflows
- Working with open biomedical datasets such as TCGA and GTEx
- Applying learned methods to translational biomarker-discovery problems
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Hands-on Lab: Build a simple multi-omics analysis workflow using R and open datasets such as TCGA or GTEx, evaluate candidate biomarkers, and connect computational findings with validation strategies.
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Tools Covered: R, OmicAnalyzer, MAST, TCGA, GTEx, ELISA, PCR, Western Blotting, Machine-Learning Evaluation Tools
