Predictive Modeling of Disease Risk Using Genomic Data – A Hands-On ML Workshop

• Understand the basics of genomics and disease prediction.

• Learn to handle and explore genomic datasets (SNPs, gene expression).
• Apply feature engineering techniques for biological data.
• Build machine learning models using scikit-learn and XGBoost.
• Evaluate models using ROC, AUC, and confusion matrix.
• Interpret models with SHAP for feature importance analysis.
• Gain skills applicable in precision medicine and biomedical research.

Day 1 – Genomic Data & ML Fundamentals

• Introduction to genomics and predictive modeling
• Data Loading and Exploration (e.g., SNP or gene expression data)
• Feature Engineering – Encode SNPs or normalize gene expression
• Train-test split & Baseline Models (Logistic Regression, Decision Trees)

Day 2 – Advanced Modeling & Interpretation

• Advanced Models – XGBoost & Random Forest
• Model Evaluation – ROC, AUC, Confusion Matrix
• SHAP-based Interpretability – Understanding top features

• Students and Researchers in biotechnology, bioinformatics, computational biology, and data science who wish to gain practical experience in applying machine learning to real genomic datasets.
• Life Science Professionals seeking to upskill in data-driven techniques for disease prediction and personalized medicine.
• AI/ML Enthusiasts interested in exploring applications of machine learning in the biomedical and healthcare domain.
• Educators and Academicians looking to integrate applied genomics and ML into their teaching or curriculum development.
• Healthcare Innovators and Entrepreneurs aiming to understand the intersection of genomics and predictive analytics for research or product development.