AI-Powered Multi-Omics Integration for Biomarker, Disease Mechanism & Therapeutic Target Discovery

• Understand the fundamentals of multi-omics data integration across genomics, transcriptomics, proteomics, metabolomics, and epigenomics.
• Learn practical approaches for data preprocessing, dimensionality reduction, feature selection, and MOFA+-based integration.
• Apply machine-learning models such as Random Forest and XGBoost for biomarker discovery and patient stratification.
• Use SHAP-based explainable AI to interpret predictive molecular features and biomarker importance.
• Perform pathway enrichment and network biology analysis to understand disease mechanisms.
• Prioritize candidate biomarkers and therapeutic targets for precision medicine and translational research.

📅 Day 1: Multi-Omics Integration & Feature Discovery

Core Objective: Understand how heterogeneous omics datasets can be processed, integrated, visualized, and transformed into meaningful molecular features for downstream biomarker discovery.

• Introduction to multi-omics and integrative biological analysis
• Major omics layers: genomics, transcriptomics, proteomics, metabolomics, and epigenomics
• Challenges in heterogeneous, high-dimensional biological datasets
• Data preprocessing, normalization, scaling, and missing-value handling
• PCA and UMAP for dimensionality reduction and visualization
• Feature selection using LASSO and Random Forest
• Multi-omics integration strategies: early, intermediate, and late integration
• MOFA/MOFA+ for latent-factor modelling
• Identification of shared and omics-specific molecular signatures
• Preparing integrated datasets for downstream AI/ML analysis

🛠️ Hands-On Lab

Preprocess and integrate a multi-omics dataset, perform PCA/UMAP-based visualization, apply feature-selection approaches, and identify informative molecular patterns using MOFA+.

🧰 Tools Covered: Python, pandas, scikit-learn, UMAP, LASSO, Random Forest, MOFA/MOFA+, Google Colab

📅 Day 2: AI/ML Biomarker Discovery & Explainable Prediction

Core Objective: Build, validate, and interpret machine-learning models for biomarker discovery, disease classification, molecular subtyping, and patient stratification.

• Preparing integrated multi-omics datasets for machine-learning analysis
• Feature engineering for biomarker-discovery workflows
• Train-test splitting and cross-validation
• Regularized models for molecular feature selection
• Random Forest and XGBoost for predictive modelling
• Disease classification and molecular-subtype prediction
• Model evaluation using ROC-AUC and PR-AUC
• Precision, recall, and F1-score for classification assessment
• Feature-importance analysis for molecular interpretation
• Explainable AI using SHAP
• Biomarker ranking and multi-omics signature development
• Patient stratification using predictive molecular features
• Preventing overfitting and data leakage in biomedical ML

🛠️ Hands-On Lab

Train and validate machine-learning models using integrated multi-omics data, evaluate predictive performance, apply SHAP explainability, and generate a ranked shortlist of candidate biomarkers.

🧰 Tools Covered: Python, pandas, scikit-learn, XGBoost, SHAP, matplotlib, Google Colab

📅 Day 3: Disease Mechanism & Therapeutic Target Prioritization

Core Objective: Translate AI-derived biomarkers into biological pathways, disease mechanisms, therapeutic targets, and clinically relevant translational insights.

• Functional interpretation of candidate biomarkers
• Gene Ontology enrichment analysis
• KEGG and Reactome pathway analysis
• Protein–protein interaction network construction
• Network-based identification of disease-associated genes
• Disease-associated pathway and mechanism interpretation
• Therapeutic-target prioritization
• Drug-response and treatment-response biomarker concepts
• Precision-medicine and patient-stratification applications
• Introduction to single-cell and spatial multi-omics
• Linking molecular signatures with cellular and tissue heterogeneity
• Computational and experimental biomarker-validation strategies
• Building an end-to-end translational multi-omics discovery workflow

🛠️ Hands-On Lab

Perform pathway and enrichment analysis on candidate biomarkers, construct biological interaction networks, interpret disease-associated mechanisms, and prioritize potential therapeutic targets.

🧰 Tools Covered: STRING, Cytoscape, Enrichr, Reactome, TCGA, GTEx, Python/R

• Integrate and analyze multi-omics datasets for biomarker discovery.
• Apply machine learning and explainable AI to identify and rank important molecular features.
• Build and evaluate predictive models for disease classification and patient stratification.
• Perform pathway enrichment and network analysis to interpret disease mechanisms.
• Prioritize candidate biomarkers and therapeutic targets for precision medicine research.
• Develop a practical, reusable AI-driven multi-omics analysis workflow for future research projects.

• Undergraduate and postgraduate students in Bioinformatics, Biotechnology, Genomics, Molecular Biology, Computational Biology, and Life Sciences
• Ph.D. scholars and researchers working with omics, biomarkers, disease mechanisms, or precision medicine
• Faculty members and academicians interested in AI-enabled multi-omics research
• Bioinformatics professionals and data analysts working with biological datasets
• Biomedical, pharmaceutical, diagnostics, and healthcare professionals
• AI/ML researchers interested in applications of machine learning in genomics and multi-omics
• Wet-lab researchers planning to transition toward computational and data-driven biological research