ANMerge: A Comprehensive and Accessible Alzheimer's Disease Patient-Level Dataset.
ANMerge: A Comprehensive and Accessible Alzheimer's Disease Patient-Level Dataset.
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DOI：
10.3233/jad-200948
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
Hofmann-Apitius M
中科院分区：
文献类型：
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作者：
Birkenbihl C;Westwood S;Shi L;Nevado-Holgado A;Westman E;Lovestone S;AddNeuroMed Consortium;Hofmann-Apitius M
可访问的数据集对阿尔茨海默病（AD）研究的进展至关重要。AddNeuroMed联盟进行了一项纵向观察队列研究，旨在发现阿尔茨海默病的生物标志物。在这项研究中，测量了广泛的数据模式选择，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。收集到的一些数据与第三方研究人员共享。然而，这些数据是不完整的、错误的，并且缺乏互操作性。为研究界提供一个可访问的、多模式的、患者水平的AD队列数据集。我们系统地解决了原始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，一个新版本的AddNeuroMed数据集。ANMerge包括来自1702名研究参与者的多模式数据，并可通过集中门户向研究界访问。ANMerge是一个信息丰富的患者级数据资源，可以作为数据驱动的AD研究的发现和验证队列，例如机器学习和人工智能方法。
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.