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ANMerge: A Comprehensive and Accessible Alzheimer’s Disease Patient-Level Dataset

 
: Birkenbihl, Colin; Westwood, Sarah; Shi, Liu; Nevado-Holgado, Alejo; Westman, Eric; Lovestone, Simon; Hofmann-Apitius, Martin

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Volltext ()

Journal of Alzheimer's disease : JAD 79 (2021), Nr.1, S.423-431
ISSN: 1875-8908
ISSN: 1387-2877
European Commission EC
FP7-JTI; 115568; AETIONOMY
Aetionomy - Organising Mechanistic Knowledge about Neurodegenerative Diseases for the Improvement of Drug Development and Therapy
Englisch
Zeitschriftenaufsatz, Elektronische Publikation
Fraunhofer SCAI ()
Alzheimers disease; data; dementia; cohort study; MRI; multimodal; biomarkers

Abstract
Background: Accessible datasets are of fundamental importance to the advancement of Alzheimers disease (AD) research. The AddNeuroMed consortium conducted a longitudinal observational cohort study with the aim to discover AD biomarkers. During this study, a broad selection of data modalities was measured including clinical assessments, magnetic resonance imaging, genotyping, transcriptomic profiling, and blood plasma proteomics. Some of the collected data were shared with third-party researchers. However, this data was incomplete, erroneous, and lacking in interoperability.
Objective: To provide the research community with an accessible, multimodal, patient-level AD cohort dataset.
Methods: We systematically addressed several limitations of the originally shared data and provided additional unreleased data to enhance the patient-level dataset.
Results: In this work, we publish and describe ANMerge, a new version of the AddNeuroMed dataset. ANMerge includes multimodal data from 1,702 study participants and is accessible to the research community via a centralized portal.
Conclusion: ANMerge is an information rich patient-level data resource that can serve as a discovery and validation cohort for data-driven AD research, such as, for example, machine learning and artificial intelligence approaches.

: http://publica.fraunhofer.de/dokumente/N-618657.html