by Admin | Oct 16, 2026 | Events, Hereditary
A scientific paper published within the HEREDITARY project has received the 1st Prize in the IEEE Engineering in Medicine and Biology Prize Paper Award, recognising its contribution to research on electroencephalography (EEG) data analysis and deep learning.
The award-winning paper, “The More, the Better? Evaluating the Role of EEG Preprocessing for Deep Learning Applications”, was published in IEEE Transactions on Neural Systems and Rehabilitation Engineering. It was authored by Federico del Pup, Andrea Zanola, Louis Fabrice Tshimanga, Alessandra Bertoldo and Manfredo Atzori, all from the Università degli Studi di Padova (UNIPD).
About the paper
The study investigates how different EEG preprocessing strategies affect the performance of deep learning models. Although preprocessing is a key step in EEG data analysis, there is still no consensus on the most effective approaches for deep learning applications.
To address this gap, the researchers evaluated 4,800 trained models across six classification tasks related to eye blinking, motor imagery, Parkinson’s disease, Alzheimer’s disease, sleep deprivation and first-episode psychosis. The results showed that models trained on raw EEG data consistently performed worst, while simpler preprocessing pipelines often performed better than more complex approaches involving automated artifact removal.
These findings provide valuable insights for future EEG research, suggesting that more extensive preprocessing does not necessarily lead to better results and highlighting the importance of carefully selecting preprocessing strategies to improve model performance and generalisability.
About the award
Following a rigorous nomination and selection process, the paper was selected for the 2025 IEEE Engineering in Medicine and Biology Prize Paper Award, receiving the first prize. The award includes a commemorative plaque and a $1,000 monetary prize to be shared among the authors.
This recognition highlights the scientific contribution of research carried out within HEREDITARY and its work at the intersection of neuroscience, data analysis and AI.
by Admin | Oct 14, 2026 | Citizen Science and Public Engagement, Hereditary
Have you ever read a test result, a visit summary or a leaflet from your doctor and felt lost in the terminology? Many people have, and that is what we want to understand better.
As part of HEREDITARY‘s citizen science work, we have launched the questionnaire “Understanding of Health-Related Information”. It is open to everyone, and your answers will feed straight into our research. It takes 10–15 minutes and you don’t need any previous medical knowledge.
Why clear words matter
Clear health information helps people make informed decisions, ask the right questions and follow treatments as intended. Medical language can be difficult to understand for patients, caregivers and the general public. HEREDITARY puts citizens at the centre of its research, and that only works when scientists and the public can understand each other.
What you will be asked to do
The questionnaire has two parts:
Part 1 – Five health-related terms. For each term, you’ll explain what it means to you, read a simplified definition, answer a few questions, and share how it could be improved.
Part 2 – You and health information. A short survey about your background and how you find, understand and use health information, including what makes it difficult and how it could be explained more clearly.
What happens with your answers
The questionnaire is a key step in a validation process designed by our partner UNL and described in the public deliverable D6.5 Citizen science and terminology: Methodology. In short:
- Terminologists write simplified definitions of medical terms.
- Health experts check their accuracy.
- Non-experts like you tell us if the definitions are clear and actually understandable.
- Definitions are refined and retested until both experts and non-experts agree on them.
- The final definitions are added to HEREDITARY’s multilingual medical terminology resource.
Your answers will help us compare how familiar people feel with a term against how well they really understand it. They will also help us identify common misunderstandings and find out which concepts are hardest to explain. The results will support recommendations for patient education, health literacy and clearer communication between healthcare professionals and the public. They will also help us design more inclusive, accessible and patient-centred communication tools for HEREDITARY.
Your privacy
Taking part is voluntary and confidential. Your data will be processed only for this study, with your consent and in line with the GDPR, and your responses will be anonymised. You can withdraw at any time without giving a reason. All the details about your personal data treatment are in the questionnaire.
Take part and share
👉 Take the questionnaire now
Know someone without expert knowledge who could take part? Please share this with friends, family, patient associations or anyone who deals with health information day to day. The more varied the voices, the better our results.
Want to know more? Read the full methodology in D6.5 and visit our Citizen Science & Public Engagement page.
by Admin | Sep 30, 2026 | Events, Hereditary
From 21 to 24 September 2026, the Conference and Labs of the Evaluation Forum (CLEF) 2026 brought together researchers, developers and students working on information access and evaluation in Jena, Germany. Hosted by Friedrich-Schiller-Universität Jena, the event provided a platform for presenting new approaches and discussing advances in information access systems.
HEREDITARY was represented by several project partners, with multiple contributions focusing on the GutBrainIE task, within the BioASQ lab, which addresses the extraction of information about the gut–brain axis from biomedical literature.
Contributions from HEREDITARY partners
Graphwise presented “Do You Need a Frontier LLM, or Can Domain-Specific Encoders Compete in Gut–Brain Information Extraction?”, exploring different approaches to named entity recognition, entity disambiguation and relation extraction. The team achieved second place in the Named Entity Recognition (NER) and Named Entity Recognition and Disambiguation (NERD) subtasks, while its Concept-level Relation Extraction system also ranked second in its respective subtask.
TU Graz also contributed to the GutBrainIE task with “CHASTE: Constrained Holistic Annotator System with onTological Entities”. The work explores a grammar-based approach to entity and relation extraction, combining language models with ontological and natural-language information. A merged run combining the TU Graz and Graphwise approaches achieved a top-precision result in one of the GutBrainIE subtasks.
Aalborg University (AAU) contributed three papers to the GutBrainIE workshop: “GutHub: Tackling the GutBrainIE Task Through Entity Recognition, Disambiguation, and Relation Extraction”, “GetGut: A Multi-Stage Pipeline for Gut-Brain Axis Information Extraction” and “Trusting Gut Instincts: A Fine-Grained Error Analysis of Transformer-Based Structured Data Extraction from Gut-Brain Axis Publications”. Together, the contributions address different aspects of information extraction and the analysis of transformer-based approaches for biomedical literature.
Advancing gut–brain information extraction
The participation of HEREDITARY partners at CLEF 2026 reflects the project’s ongoing work on extracting, structuring and connecting information from biomedical literature, contributing to richer knowledge resources around the gut–brain axis and neurological and neurodegenerative diseases.
by Admin | Sep 28, 2026 | Events, Hereditary
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:
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.
by Admin | Aug 6, 2026 | Hereditary
The University of Colorado Anschutz Medical Campus (CU Anschutz), HEREDITARY’s only U.S. partner, has published a new article showcasing its contribution to the project and the potential of AI to support the early detection of Parkinson’s disease through retinal imaging.
Within HEREDITARY, researchers at the Sue Anschutz-Rodgers Eye Center are investigating whether AI can identify retinal biomarkers associated with neurodegenerative diseases such as Parkinson’s disease and multiple sclerosis. By combining retinal images with other clinical and biomedical data, the team aims to develop multimodal AI models that could contribute to earlier, less invasive diagnosis.
The article also highlights the role of federated learning, a key approach within HEREDITARY that enables institutions to collaboratively train AI models while keeping sensitive patient data securely at their original locations. This privacy-preserving methodology allows researchers across the consortium to build more robust and reliable models without sharing raw clinical data.
As HEREDITARY’s only U.S.-based partner, CU Anschutz brings internationally recognised expertise in ophthalmic AI, oculomics, and federated learning, strengthening the consortium’s efforts to better understand the gut-brain axis and advance research into neurodegenerative diseases.
Want to learn more about CU Anschutz’s contribution to HEREDITARY? Read the full article on their website. READ NOW!
by Admin | Jul 29, 2026 | Hereditary
Artificial Intelligence is transforming biomedical research, but innovation alone is not enough. To unlock the full potential of health data, researchers must also ensure that new technologies are developed responsibly, transparensively, and in full alignment with Europe’s evolving legal and ethical framework.
This is the focus of HEREDITARY Deliverable 7.2, an in-depth legal and ethical study that moves beyond theoretical compliance to analyse how European legislation applies to the project’s real-world research activities and federated architecture. Building on the foundations established in Deliverable 7.1, the study assesses how HEREDITARY’s technologies align with the latest European regulations while identifying the challenges that must still be addressed as the project progresses.
GDPR remains at the heart of HEREDITARY
One of the main conclusions of the study is that the General Data Protection Regulation (GDPR) continues to be the cornerstone governing the project’s processing of health and genetic data.
HEREDITARY’s federated approach supports several key GDPR principles, including data minimisation, privacy by design, and reducing the need to transfer sensitive information between institutions. By keeping patient data within each participating organisation, the project significantly reduces many traditional data-sharing risks.
However, federated learning is not automatically GDPR compliant. Even when raw data never leaves local institutions, model parameters, analytical outputs or derived inferences may still reveal personal information under certain circumstances. For this reason, GDPR compliance must be ensured throughout the entire data lifecycle, including data collection, local processing, federated model training, analytics, output generation, storage, and cross border collaboration.
Preparing for the European Health Data Space
Another major focus of Deliverable 7.2 is the European Health Data Space (EHDS), one of the EU’s flagship initiatives for enabling secure secondary use of health data across Europe.
The analysis concludes that HEREDITARY is already strongly aligned with many of the EHDS objectives. Its emphasis on interoperability, privacy-preserving data analysis and federated infrastructures positions the project as a promising contributor to future European health data ecosystems, including potential integration with HealthData@EU infrastructures.
AI Act: responsible AI goes beyond today’s requirements
Because HEREDITARY currently operates as a research infrastructure, many obligations introduced by the AI Act are likely to remain limited during the project’s research phase. Nevertheless, the Deliverable stresses that this situation is dynamic. If AI components developed within HEREDITARY eventually evolve into clinical decision-support tools or become deployed in operational healthcare settings, additional regulatory requirements related to risk management, transparency, human oversight and conformity assessment could become applicable.
This forward-looking assessment allows the consortium to anticipate future obligations long before technologies reach clinical practice.
DGA & NIS2: Governance and cybersecurity as key enablers
The Data Governance Act (DGA) reinforces principles that closely match HEREDITARY’s federated design, promoting secure processing environments, interoperability, responsible reuse of protected datasets and transparent governance mechanisms. Meanwhile, the NIS2 Directive highlights the growing importance of cybersecurity in distributed health data infrastructures.
Federated learning offers important privacy advantages, but it also introduces new types of cybersecurity risks, including model poisoning, inference attacks and parameter leakage. The study concludes that cybersecurity, data protection and governance must be addressed as an integrated framework, recognising that protecting health data is both a legal obligation and a prerequisite for trustworthy biomedical research.
Looking beyond compliance: ethics in federated AI
HEREDITARY combines genomics, artificial intelligence and cross-border health research. This makes ethical governance essential throughout the project. The study identifies ongoing challenges related to fairness, transparency, explainability, accountability, informed participation and responsible stewardship of research data.
Federated architectures substantially reduce the need for centralised data sharing, but they do not eliminate ethical risks associated with bias, re-identification, opaque AI models or the downstream use of research outputs. These issues require continuous assessment as both technologies and regulations evolve.
Bringing regulation closer to real-world research
One of the most valuable contributions of Deliverable 7.2 is its Use Case-Specific Analysis, presented in Annex 3.
Instead of relying on abstract legal interpretation, WP7 evaluates how European regulations apply to the concrete research scenarios developed across HEREDITARY’s five clinical use cases. It connects legal analysis directly with real technical workflows. This practical assessment also addresses one of the consortium’s previously identified governance gaps: situations where one partner’s analytical tools are applied to another partner’s datasets within the federated infrastructure.
These findings will now guide the final phase of Work Package 7, where the consortium will identify the remaining regulatory and ethical gaps and develop concrete recommendations to ensure that the HEREDITARY framework remains not only innovative, but also compliant, trustworthy and sustainable for future healthcare research across Europe.
Recent Comments