1. ANMerge: A Comprehensive and Accessible Alzheimer’s Disease Patient-Level Dataset
- Author
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Sarah Westwood, Martin Hofmann-Apitius, Eric Westman, Colin Birkenbihl, Simon Lovestone, Alejo J. Nevado-Holgado, and Liu Shi
- Subjects
Male ,Proteomics ,Genotype ,Computer science ,Multimodal data ,Interoperability ,Datasets as Topic ,Cohort Studies ,03 medical and health sciences ,0302 clinical medicine ,Alzheimer Disease ,medicine ,Humans ,dataset ,Dementia ,Profiling (information science) ,Aged ,030304 developmental biology ,Aged, 80 and over ,0303 health sciences ,Modalities ,data-driven science ,Gene Expression Profiling ,General Neuroscience ,multimodal ,biomarkers ,Disease patient ,General Medicine ,AddNeuroMed ,cohort analysis ,medicine.disease ,Magnetic Resonance Imaging ,Data science ,3. Good health ,genome wide association studies ,Psychiatry and Mental health ,Clinical Psychology ,Cohort ,Female ,Geriatrics and Gerontology ,Alzheimer’s disease ,030217 neurology & neurosurgery ,Research Article ,dementia ,Cohort study - Abstract
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. 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 resources and provided additional unreleased data to enhance the 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.
- Published
- 2021
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