About Workshop
This three-day workshop provides a practical introduction to the integration of artificial intelligence, genomic medicine, and precision oncology. Participants will explore cancer genomics, next-generation sequencing, multi-omics analysis, biomarker discovery, variant interpretation, AI-assisted diagnosis, drug discovery, and personalised therapy. Through guided demonstrations using publicly available cancer databases, bioinformatics platforms, Google Colab, AutoML tools, and generative AI, participants will understand how complex genomic data can be translated into clinically meaningful insights.
Aim
To provide participants with foundational knowledge and practical exposure to artificial intelligence and genomic medicine for cancer diagnosis, biomarker discovery, patient stratification, and precision therapy development.
What Participants Will Learn
- Understand the biological and genomic foundations of cancer.
- Explore the applications of machine learning, deep learning, generative AI, and large language models in oncology.
- Understand how genomic, transcriptomic, proteomic, epigenomic, and clinical data are integrated.
- Analyse and interpret cancer-related genomic variants and biomarkers.
- Explore AI applications in digital pathology, medical imaging, drug discovery, and treatment selection.
- Gain practical exposure to public cancer databases and computational platforms.
- Recognise ethical, regulatory, privacy, and bias-related challenges in AI-enabled healthcare.
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
📅 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
📅 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
Important Dates
Registration Ends
5: 30 PM IST
Workshop Dates
2026-08-13
06:30 PM IST
06:30 PM IST
What You Will Gain
Outcomes
- Understand the principles of cancer genomics and precision medicine.
- Apply AI and machine learning techniques to analyse genomic and multi-omics datasets.
- Interpret genomic variants and identify clinically relevant biomarkers.
- Use public cancer genomic databases and bioinformatics platforms effectively.
- Explore AI applications in cancer diagnosis, prognosis, and treatment selection.
- Understand the role of AI in drug discovery, pharmacogenomics, and personalised therapy.
- Recognise ethical, legal, and regulatory considerations in AI-enabled genomic medicine.
- Gain practical experience with computational tools used in precision oncology research and clinical practice.
Who Should Attend
- Graduate and postgraduate students
- Ph.D. scholars and research fellows
- Faculty members and academicians
- Biotechnology and bioinformatics researchers
- Life-science and biomedical professionals
- Clinicians, oncologists, and pathologists
- Genomics and molecular-diagnostics professionals
- Healthcare data scientists
- Pharmaceutical and biotechnology industry professionals
- Professionals working in precision medicine, drug discovery, or clinical research