📅 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
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Hands-on Lab: Explore publicly available cancer genomic datasets, retrieve patient-level molecular information, and examine cancer-associated genes, mutations, variants, and expression profiles.
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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
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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.
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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
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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.
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Tools Covered: ChatGPT, Google Colab, AutoML Platforms, AI-Based Cancer Prediction Tools
