AI-Powered Multi-Omics Data Integration for Biomarker Discovery

📅 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

🛠️ 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. 🧰 Tools Covered: Python, Jupyter Notebook, PCA, t-SNE, UMAP, Lasso, Random Forest, Multi-Omics Datasets

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📅 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

🛠️ 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. 🧰 Tools Covered: Python, Jupyter Notebook, MOFA, DeepMOCCA, Galaxy, Bioconductor, OmicSoft, scikit-learn, TensorFlow, PyTorch, SMOTE

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📅 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

🛠️ 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. 🧰 Tools Covered: R, OmicAnalyzer, MAST, TCGA, GTEx, ELISA, PCR, Western Blotting, Machine-Learning Evaluation Tools