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ANMerge: A comprehensive and accessible Alzheimer's disease patient-level dataset

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

Posted 06 Aug 2020
medRxiv DOI: 10.1101/2020.08.04.20168229

Background: Accessible datasets are of fundamental importance to the advancement of Alzheimer's 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. Methods: We systematically addressed several limitations of the originally shared data and provide 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 1702 study participants and is accessible to the research community via a centralized portal. Conclusions: 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. ANMerge can be downloaded here: https://doi.org/10.7303/syn22252881

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