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SUMMARY:Generative AI for Life Science
DESCRIPTION:Register latest by: October 6\, 2026\nAbout the event:\nGenerative AI has become both highly prevalent and highly divisive. While public opinion varies\, rapid advancement in GenAI is transforming the life sciences\, enabling new approaches to understand biological systems\, predict molecular structures and advance clinical diagnostics. \nIn this workshop\, we will demonstrate how GenAI is applied across key life science domains through four interactive sessions. Participants will train models\, explore biological datasets and generate real outputs such as synthetic cell images\, protein structure predictions\, and mRNA sequences for vaccines. By the end of the workshop\, you will have hands-on experience in developing GenAI techniques and applying them to solve real-world life science problems. \nWho is this for?\nThis workshop is intended for: \n\n\nResearchers\, computational biologists and data scientists working in life science\, biomedical research\, or healthcare. \n\n\nAI/ML practitioners interested in biological or medical applications. \n\n\nUsers of imaging\, omics or clinical datasets who want to apply generative models. \n\n\nAnyone with an interest in beneficial GenAI\, interpretable models and multimodal learning in science. \n\n\nBeginners to generative AI who want a practical\, application-focused introduction. \n\n\nKey takeaways for participants:\n\n\nUnderstand what generative AI is and how generative models (autoencoders\, transformers and multimodal systems) are applied to biological data. \n\n\nHands-on experience training and using models for: \n\n\nImage analysis and reconstruction (autoencoders). \n\n\nProtein feature extraction and structure prediction (protein language models). \n\n\nDrug discovery (codon prediction). \n\n\n\n\nUnderstand key concepts such as latent space representations\, intermediate embeddings and attention mechanisms. \n\n\nExperience with real biomedical datasets including X-ray images and protein sequences. \n\n\nMethods for evaluating models with practical metrics\, visualisations and interpretability tools (e.g. saliency maps). \n\n\nPrerequisites:\nBasic familiarity with Python is expected. Experience with machine learning is preferable but no prior experience with generative AI is required. \nParticipants should: \n\n\nHave a laptop with Windows\, macOS or Linux. \n\n\nBe able to access a web browser and use Google Colab. \n\n\n  \nInstructors and Teaching Assistants\n\n\nLuca Panconi – AI Application Specialist in the Life Science Team at Sweden AI Factory. \n\n\nIvan Cucchi – AI Application Specialist in the Life Science Team at Sweden AI Factory. \n\n\nAshwin Mohanan – Training Team Coordinator at Sweden AI Factory. \n\n\nSchedule\nOctober 7\, 2026 \n\n\n\n\nTime\nContent\n\n\n\n15:00–16:50pm \n\n\nPre-workshop setup \n\n\n\n\n\nOctober 8\, 2026 \n\n\n\n\n\n9:00–9:15am \n\n\nWelcome and Icebreaker \n\n\n\n\n9:15–10:30am \n\n\nSession 1: An Introduction to Generative AI \n\n\n\n\n10:30–10:45am \n\n\nBreak and Questions \n\n\n\n\n10:45–12:00pm \n\n\nSession 2: Interpretable Autoencoders for Image Classification \n\n\n\n\n12:00–13:15pm \n\n\nLunch Break \n\n\n\n\n13:15–14:30pm \n\n\nSession 3: Protein Language Models \n\n\n\n\n14:30–14:45pm \n\n\nBreak and Questions \n\n\n\n\n14:45–16:00pm \n\n\nSession 4: An Application to Drug Discovery \n\n\n\n\n16:00–16:30pm \n\n\nConcluding Remarks and Final Questions \n\n\n\n\n\n 
URL:https://swedenaifactory.se/event/generative-ai-for-life-science/
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