AI-Powered Cancer Genomics and Precision Medicine: From Diagnosis to Personalized Treatment

Aim

Workshop Objectives

Workshop Structure

📅 Day 1: Foundations of AI and Cancer Genomics
Core Objective: Build a strong foundation in cancer biology, genomic technologies, and artificial intelligence approaches used to analyse complex cancer genomic and transcriptomic data.

• Fundamentals of cancer biology and cancer genomics
• Hallmarks of cancer and tumour heterogeneity
• Introduction to artificial intelligence in healthcare and oncology
• Fundamentals of machine learning and deep learning
• Generative AI and Large Language Models (LLMs) in cancer genomics and transcriptomics
• Introduction to Next-Generation Sequencing (NGS) technologies
• Whole Genome Sequencing (WGS) and Whole Exome Sequencing (WES)
• RNA sequencing for cancer transcriptomic analysis
• Single-cell sequencing applications in cancer research

🛠️ Hands-on Lab: Explore publicly available cancer genomic datasets, retrieve patient-level molecular information, and examine cancer-associated genes, mutations, variants, and expression profiles.

🧰 Tools and Databases Covered: TCGA, cBioPortal, COSMIC, GEO, ClinVar, Genome-Wide Analysis Tools, SNP Analysis Tools

📅 Day 2: AI-Driven Cancer Diagnosis and Biomarker Discovery
Core Objective: Apply artificial intelligence and multi-omics analysis to identify clinically relevant cancer biomarkers and support data-driven diagnosis and precision-oncology decisions.

• Transforming genomic data into clinically actionable insights
• From genomic analysis to clinical decision-making
• AI-based integration of genomics and transcriptomics data
• Integration of proteomics, epigenomics, and metabolomics datasets
• AI-based biomarker discovery for early cancer detection
• Identification of prognostic and predictive biomarkers
• AI applications in medical imaging and digital pathology
• Radiomics, histopathology, and biomedical image analysis
• Precision oncology and molecular tumour boards
• Clinical Decision Support Systems for cancer diagnosis and treatment planning

🛠️ Hands-on Lab: Perform cancer-variant calling and annotation, explore genomic alterations through genome browsers, prioritise clinically relevant variants using AI-assisted approaches, and investigate gene-expression patterns in cancer datasets.

🧰 Tools Covered: Variant Calling and Annotation Tools, Genome Browsers, AI-Based Variant Prioritisation Tools, GEPIA

📅 Day 3: AI for Precision Cancer Therapy and Future Directions
Core Objective: Explore how AI, pharmacogenomics, drug discovery, immunotherapy, and predictive modelling can enable personalised cancer treatment while addressing clinical, ethical, and regulatory challenges.

• Translating artificial intelligence models into clinical oncology practice
• AI applications in precision medicine and personalised cancer care
• Patient stratification and cancer-risk prediction models
• Pharmacogenomics and prediction of individual treatment response
• AI-assisted drug discovery and drug repurposing
• Target identification and virtual screening for anticancer therapeutics
• Digital twins and AI-enabled clinical-trial optimisation
• Personalised cancer therapy and treatment selection
• Immunotherapy and CAR-T cell therapy
• CRISPR-based gene editing and AI-guided therapeutic design
• Ethical, regulatory, and clinical challenges in AI-enabled oncology
• Cancer-data privacy, security, and responsible data governance
• Bias, fairness, explainability, and regulatory frameworks for medical AI
• Future directions of artificial intelligence in oncology

🛠️ Hands-on Lab: Use AI tools for genomic medicine, apply ChatGPT to biomedical research tasks, and develop an introductory AI-based cancer-prediction workflow using Google Colab and AutoML platforms.

🧰 Tools Covered: ChatGPT, Google Colab, AutoML Platforms, AI-Based Cancer Prediction Tools

Who Should Enrol?

Important Dates

Workshop Outcomes

Meet Your Mentor(s)

Prof. Kumud Malhotra

Professor and Dean

Biotechnology

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