Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin, Germany.
Bonn-Aachen International Center for IT, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany.
J Alzheimers Dis. 2021;79(1):423-431. doi: 10.3233/JAD-200948.
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.
可访问的数据集对于推进阿尔茨海默病（AD）研究至关重要。AddNeuroMed 联盟进行了一项纵向观察性队列研究，旨在发现 AD 生物标志物。在这项研究中，广泛选择了多种数据模式进行测量，包括临床评估、磁共振成像、基因分型、转录组分析和血浆蛋白质组学。收集的一些数据与第三方研究人员共享。然而，这些数据不完整、错误且缺乏互操作性。
为研究界提供一个可访问的、多模态的、患者层面的 AD 队列数据集。
我们系统地解决了最初共享资源的几个限制，并提供了额外的未发布数据来增强数据集。
在这项工作中，我们发布并描述了 ANMerge，这是 AddNeuroMed 数据集的一个新版本。ANMerge 包含 1702 名研究参与者的多模态数据，并通过集中式门户向研究界提供访问权限。
ANMerge 是一个信息丰富的患者层面数据资源，可作为数据驱动的 AD 研究（例如机器学习和人工智能方法）的发现和验证队列。
