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

Harness the Power of Genomics and AI to Predict Health Outcomes

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MODE
Virtual / Online

TYPE
Mentor Based

LEVEL
Moderate

DURATION
2 Days (1.5 Hours per Day)

START DATE
22 -July -2025

TIME
04:00 PM IST

About

The Predictive Modeling of Disease Risk Using Genomic Data workshop is a hands-on, online training program designed to teach participants how to apply machine learning techniques to real-world genomic datasets for disease prediction. Through practical exercises using Python and tools like scikit-learn and SHAP on Google Colab, attendees will learn to preprocess genetic data, build predictive models, and interpret results with biological relevance. Ideal for students, researchers, and professionals in life sciences, bioinformatics, and AI, this workshop bridges the gap between genomics and data science, empowering participants to contribute to the future of precision medicine.

Aim

To equip participants with practical skills in applying machine learning techniques to genomic datasets for disease risk prediction, enabling them to interpret genetic information, build predictive models, and contribute to the future of precision medicine.

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Workshop Objectives

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.

Workshop Structure

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

Intended For

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.

Important Dates

Registration Ends

2025-07-22
Indian Standard Timing 3:00 PM

Workshop Dates

2025-07-22 to 2025-07-24
Indian Standard Timing 04:00 PM

Workshop Outcomes

Participants will:

✅ Gain hands-on experience in loading, processing, and modeling genomic data using Python.
✅ Understand how to apply machine learning models (Logistic Regression, Random Forest, XGBoost) for predicting disease risk.
✅ Learn how to evaluate models using metrics like ROC, AUC, and confusion matrices.
✅ Be able to interpret models using SHAP to identify key genomic features influencing disease outcomes.
✅ Receive reusable notebooks and datasets for future projects and research.
✅ Build a foundation for entering interdisciplinary careers that integrate biology, data science, and AI.
✅ Earn a certificate of participation (if included), useful for resumes and academic portfolios.

Fee Structure

Student Fee

INR. 1199
USD. 45

Ph.D. Scholar / Researcher Fee

INR. 1499
USD. 50

Academician / Faculty Fee

INR. 1999
USD. 55

Industry Professional Fee

INR. 2499
USD. 75

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