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E-DADS is an international research team bridging Computer Science and Neurology,
led by University College London.

Funding: EU Joint Programme --- Neurodegenerative Disease Research, 2020--2023


Alzheimer’s disease (AD) is a global health and economic burden with currently about 47 million affected individuals worldwide. No provably disease-modifying treatments exist (as of 2020). Delaying disease onset in dementia patients by five years can reduce care costs by 36% about 88B euro per year across the EU. A key confound preventing successful outcomes in most treatment trials to date has been AD’s high variation in onset, mechanism, and clinical expression.

E-DADS aims to untangle this heterogeneity by defining data-driven subtypes of the clinical manifestation of AD based on brain imaging, cognitive markers, and fluid biomarkers that are robustly identifiable from predictive risk factors (genetics, co-morbidities, physiological and lifestyle factors) years before disease onset.

To achieve this we develop a novel multi-view learning strategies that relates end-stage disease manifestations observable in clinical cohorts to features of early-stage or at-risk individuals in preclinical cohorts and the general pre-affected population from population or aging studies. This approach is only possible now due to the availability of large population data, richly phenotyped AD cohorts and advances in machine learning. E-DADS uniquely assembles the necessary data and expertise. The ability to identify AD subtypes and predict them years before onset will significantly advance AD research and clinical management via precision medicine. First, it identifies distinct homogeneous groups, shedding new light on that nature and variability of disease mechanisms ultimately pinpointing effective drug targets. Second, it enables enrichment of future clinical trials for specific groups of patients likely to benefit from a particular intervention. Third, it highlights potential lifestyle interventions that may affect or delay disease onset at very early stages.

E-DADS delivers the underpinning technology to achieve this through machine learning and big data analytics together with a prototype software tool enabling future translation and uptake.

Selected outputs

Journal articles


E-DADS partners

Widening partner

E-DADS Collaborators

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