AI in Cancer Research: Multi-Omics, Biomarker Discovery & Precision Medicine
Harness Artificial Intelligence to Decode Cancer Biology, Discover Molecular Biomarkers, and Enable Data-Driven Precision Medicine
About This Course
This workshop introduces participants to AI-driven cancer research workflows, covering the integration of genomics, transcriptomics, proteomics, epigenomics, and clinical data for molecular profiling, biomarker discovery, and precision medicine applications.
Participants will explore real-world cancer datasets from resources such as TCGA, GEO, and cBioPortal, learn machine learning approaches for identifying molecular signatures, and understand how AI models support patient stratification, therapeutic target discovery, and personalized cancer research.
Aim
To provide researchers and professionals with practical knowledge of AI-based approaches for integrating multi-omics data, discovering cancer biomarkers, and applying computational strategies in precision oncology research.
Workshop Objectives
- Understand the role of AI in modern cancer research and precision medicine.
- Explore different cancer omics layers including genomics, transcriptomics, proteomics, and clinical data.
- Learn workflows for processing and integrating large-scale cancer datasets.
- Apply machine learning approaches for biomarker discovery and molecular classification.
- Analyze cancer datasets using public repositories such as TCGA, GEO, and cBioPortal.
- Understand AI-driven approaches for patient stratification and therapeutic research.
- Interpret molecular signatures and biological insights from computational analyses.
Workshop Structure
Day 1: AI & Multi-Omics Foundations in Cancer Research
- Introduction to AI applications in modern cancer research
- Understanding cancer biology through molecular and clinical data
- Overview of multi-omics technologies: Genomics, Transcriptomics, Proteomics, Epigenomics, Clinical data integration
- Understanding cancer datasets and public resources: TCGA, GEO, cBioPortal
- Fundamentals of AI and machine learning workflows for cancer data analysis
- Challenges in integrating heterogeneous biological and clinical datasets
Hands-on Session:
Explore cancer omics datasets, perform data preprocessing, and visualize molecular profiles from publicly available cancer datasets.
Tools:
Python | Google Colab | Pandas | NumPy | Matplotlib | TCGA/GEO
Day 2: AI-Based Biomarker Discovery & Cancer Molecular Profiling
- Understanding cancer biomarkers and molecular signatures
- Machine learning approaches for biomarker identification
- Feature selection and dimensionality reduction techniques
- Integrating multi-omics features for cancer classification
- Combining molecular profiles with clinical characteristics: Patient demographics, Disease stage, Survival information, Treatment response data
- Pathway and network-based interpretation of AI-generated biomarkers
Hands-on Session:
Develop a machine learning workflow to identify potential cancer biomarkers using molecular and clinical datasets.
Tools:
Python | Scikit-learn | Random Forest | XGBoost | PCA | SHAP
Day 3: AI-Driven Precision Medicine & Personalized Cancer Insights
- Multi-omics and clinical data integration for precision oncology
- AI-based patient stratification and cancer subtype classification
- Predicting treatment response and patient outcomes using machine learning
- Explainable AI (XAI) for understanding cancer predictions
- Applications of AI in: Biomarker-guided therapy selection, Drug response prediction, Target identification, Personalized therapeutic strategies
Hands-on Session:
Build and interpret an AI-based cancer prediction model by integrating molecular features with clinical parameters and explain predictions using XAI approaches.
Tools:
Python | Machine Learning Models | SHAP | Bioinformatics Databases
Who Should Enrol?
- Cancer Biology & Oncology Research
- Molecular Biology
- Genetics & Genomics
- Bioinformatics
- Computational Biology
- Biomedical Sciences
- Translational Medicine
- Precision Medicine
Important Dates
Registration Ends
September 24, 2026
IST 4:30 PM
Workshop Dates
September 24, 2026 – September 26, 2026
IST 5:00 PM
Workshop Outcomes
- Develop an understanding of AI-powered cancer data analysis workflows.
- Perform basic preprocessing and visualization of cancer multi-omics datasets.
- Identify potential molecular biomarkers using machine learning approaches.
- Understand feature selection, dimensionality reduction, and predictive modelling strategies.
- Explore relationships between molecular profiles and clinical outcomes.
- Interpret multi-omics findings for cancer biology and precision medicine applications.
- Gain practical exposure to computational tools used in AI-driven oncology research.
Fee Structure
Student Fee
₹2499 | $65
Ph.D. Scholar / Researcher Fee
₹3499 | $75
Academician / Faculty Fee
₹4499 | $85
Industry Professional Fee
₹5499 | $105
What You’ll Gain
- Live & recorded sessions
- e-Certificate upon completion
- Post-workshop query support
- Hands-on learning experience
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