ANMerge: A Comprehensive and Accessible Alzheimer's Disease Patient-Level Dataset.
AddNeuroMed
Alzheimer’s disease
biomarkers
cohort analysis
cohort studies
data-driven science
dataset
dementia
genome wide association studies
magnetic resonance imaging
multimodal
Journal
Journal of Alzheimer's disease : JAD
ISSN: 1875-8908
Titre abrégé: J Alzheimers Dis
Pays: Netherlands
ID NLM: 9814863
Informations de publication
Date de publication:
2021
2021
Historique:
pubmed:
9
12
2020
medline:
28
9
2021
entrez:
8
12
2020
Statut:
ppublish
Résumé
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. To provide the research community with an accessible, multimodal, patient-level AD cohort dataset. We systematically addressed several limitations of the originally shared resources and provided additional unreleased data to enhance the dataset. 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. 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.
Sections du résumé
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.
Identifiants
pubmed: 33285634
pii: JAD200948
doi: 10.3233/JAD-200948
pmc: PMC7902946
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
423-431Subventions
Organisme : Medical Research Council
ID : MC_PC_17215
Pays : United Kingdom
Organisme : Department of Health (NIHR)
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