Biological discovery is undergoing a transformation driven by the convergence of advanced imaging technologies, data science, and computational modeling. Modern microscopy and multi-scale imaging techniques now reveal structural and functional insights from the molecular to tissue levels. At the same time, artificial intelligence (AI) and physics-based models are providing powerful frameworks for interpreting and predicting biological behavior. Despite these advances, integrating these diverse methods remains a significant challenge.

This Keystone Symposium will address the critical need to integrate heterogeneous imaging modalities, aided by computational and Deep Learning-driven approaches, to derive mechanistic and predictive insights from complex biological data. The meeting aims to foster dialogue among imaging scientists, computational biologists, data scientists, and systems biologists to define emerging frontiers in data integration, standardization, accessibility, and visualization.

Participants will have the opportunity to establish new cross-disciplinary collaborations, learn about integration and validation strategies, and collectively discuss new roadmaps and directions for the field. These will enable the establishment of standard data formats and acquisition modalities for large datasets, ready for Deep Learning approaches.

Previous conferences have often centered on individual structural biology techniques, but have rarely emphasized interdisciplinary approaches. While these meetings typically acknowledge the importance of diverse methodologies, they neglect to address the practical integration of these approaches. In contrast, we propose a carefully curated program that prioritizes lectures and abstracts explicitly focused on integrative approaches, ensuring that the symposium moves beyond acknowledgment to actionable convergence.