HEREDITARY presents four papers and receives the Best Conference Paper Award at VCBM 2026

HEREDITARY had a strong presence at the 16th Eurographics Symposium on Visual Computing for Biology and Medicine (VCBM 2026), held in Ljubljana, Slovenia, from 15 to 18 September. The event brought together researchers working at the intersection of visual computing, biology and medicine, providing a relevant forum for several of the topics addressed within the HEREDITARY project.

The project presented four papers at the symposium, as part of Work Package 5 (WP5), covering different approaches to the visual exploration and analysis of biomedical and neurological data:

• “Exploring Genes in the Brain through Multimodal Views driven by Disorder Ontologies”, by Benedikt Kantz et al.
• “Interactive 3D Exploration of Large-Scale Neuronal Network Simulations with a Graph Lens”, by Peter Waldert et al.
• “GutBrainViz: Interactive Exploration of Linked ICA Components across Brain Networks and Gut Microbiota”, by Casper van Leeuwen.
• “EEGlass: A Fully Interpretable EEG Dementia Screening Assistant”, by Peter Waldert et al.

Recognition for the HEREDITARY team

The strong presence of HEREDITARY at VCBM 2026 was also marked by an important recognition. The paper “EEGlass: A Fully Interpretable EEG Dementia Screening Assistant” received the Best Conference Paper Award.

Presented by members of WP5, it introduces an interpretable visual analytics assistant for EEG-based dementia screening, supporting experts in exploring and understanding relevant information during the screening process.

In addition to the four papers, GIBBER and EEGlass were also accompanied by posters, giving participants an opportunity to explore the work in more detail.

HEREDITARY demos attract interest at VCBM

The HEREDITARY project also brought its interactive demonstrations to VCBM, with the project’s demo platform receiving positive interest from the conference community.

Through these contributions, HEREDITARY showcased how visual computing and data-driven approaches can support the exploration of complex biomedical and neuroscience datasets, particularly in the context of neurological and neurodegenerative diseases.