This MOOC explores the transformative role of artificial intelligence (AI) in accelerating the development of next-generation vaccines, with a focus of anti-cancer immunization. Adopting an interdisciplinary approach, the MOOC demonstrates how cutting-edge machine learning and deep learning techniques are being applied to optimize every stage of the vaccine pipeline: from antigen selection and epitope prediction to the discovery of novel adjuvants. Overall, this MOOC will examine how emerging technologies are reshaping the landscape of vaccine development, making it faster, more precise, and more accessible.
The course is organized into six modules that guide you through an up-to-date, interdisciplinary journey on applying artificial intelligence to the development of cancer vaccines and innovative immunotherapies. The focus is on integrating advanced computational methods, immunological techniques, and cellular profiling tools, intending to provide a comprehensive understanding of AI’s potential to enhance every stage of the vaccine development pipeline.
Module 1 – Introduction to Anti-Cancer Immunotherapies
Module 2 – AI in Antigen Discovery and Vaccine Design
Module 3 – AI in Adjuvant Selection and Immune Response Enhancement
Module 4 – Monitoring Immune Responses: Flow Cytometry Basics
Module 5 – AI-Enhanced Flow Cytometry and Immunophenotyping
Module 6 – Future Perspectives and Integration of AI in Anti-Cancer Immunotherapy
Module 7: CAR-T Cell Therapy and Artificial Intelligence
Within each module, you will find several lessons featuring demonstration videos, in-depth materials, slides, and self-assessment quizzes. The course is designed to provide both practical and conceptual skills useful in research, preclinical, and clinical development settings, with a particular focus on the use of advanced digital tools and intelligent algorithms for the design and evaluation of immunotherapies.
At the end of the course, the student will be able to:
Knowledge and Understanding
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Describe the fundamental principles of immunotherapy and the biological foundations of the immune system.
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Identify the main tools and approaches of artificial intelligence applied to immunology.
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Explain how digital technologies (AI, big data, predictive models) contribute to the design of innovative vaccines and immunological therapies.
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Interpret case studies and real-world scenarios from international research projects in the field of personalized immunotherapy.
Ability to Apply Knowledge and Understanding
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Analyze digital models for simulating immune response.
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Design integrated experimental pipelines that incorporate AI, omics technologies, and imaging for advanced immunological studies.
Autonomy of Judgment
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Critically evaluate the potential and limitations of using AI in immunotherapy.
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Discuss ethical and regulatory implications of employing intelligent algorithms in human health.
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Argue, with concrete examples, the effectiveness of emerging technologies in the personalization of immunological therapies.
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Reflect on the impact of digital innovation on precision medicine.
Communication Skills
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Clearly and effectively present results of immunological analyses or AI-applied health projects.
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Produce scientific and outreach materials (slides, short reports, infographics) illustrating AI applications in immunotherapy.
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Explain complex concepts to non-specialist audiences, fostering interdisciplinary dialogue.
Learning Skills
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Independently select up-to-date scientific sources on AI, immunotherapy, and digital technologies.
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Connect learned content with other disciplinary areas (biotechnology, ethics, public health, data science).
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Design personal or professional development pathways based on the integration of immunology and artificial intelligence.
Passing quizzes consisting of true/false and/or multiple-choice questions.
Basic knowledge of biology and immunology
Laura Conti
Federica Riccardo
Teresa Manzo
laura.conti@unito.it
federica.riccardo@unito.it
teresa.manzo@unito.it
For technical issues:
edvancedeh@unito.it