DyHealthNet: A Platform for Dynamic Exploration of the Cooperative Health Research in South Tyrol Study Data via Multi-Level Network Medicine

南蒂罗尔州合作健康研究（CHRIS）研究提供了一个全面的概述，在中部和上部瓦尔Venosta的13,000多名成年人的健康状况。这是意大利最大的基于人群的分子研究，纵向观察调查与年龄相关的常见慢性病的遗传和分子基础及其与普通人群中生活方式和环境的相互作用。在CHRIS中，基因组学和代谢组学等分子特征分析数据与重要的基线临床和生活方式数据相结合，为了解可能导致临床并发症或表明疾病流行或早期发作的生理变化及其分子基础提供了巨大的机会。以疾病为重点的研究通常有一个明确的假设，要求进行必要的统计分析，而基于人群的队列（如CHRIS）则更通用，既可以检验现有的假设，也可以根据现有数据的统计学显著性相关性生成新的假设。理想情况下，这种类型的探索性分析对不一定具有数据分析或机器学习经验的生物医学研究人员开放。基于网络的方法非常适合研究异构生物医学数据，从而产生了网络医学领域。然而，迄今为止，网络医学技术主要用于侧重于个别疾病的研究。不存在用于探索性分析基于人群的队列数据的网络平台。在DyHealthNet中，我们将缩小这一差距，并开发一个基于网络的数据分析平台，该平台将允许整合异构数据，并支持对CHRIS研究数据的动态生成子集进行探索性数据分析。为了充分利用可用多级数据的潜力，DyHealthNet平台结合了（1）使用标准化医疗信息模型（HL 7 FHIR）的数据集成，（2）用于可扩展动态分析的创新索引结构，（3）机器学习和（4）可视化分析。DyHealthNet将使CHRIS人群队列数据可用于最先进的隐私保护，基于网络的数据分析。因此，DyHealthNet将能够挖掘精准医疗的特定背景病理机制，并将作为全球多水平队列数据动态探索分析的蓝图。

The Cooperative Health Research in South Tyrol (CHRIS) study offers a comprehensive overview of the health state of >13,000 adults in the middle and upper Val Venosta. It is the largest population-based molecular study in Italy with a longitudinal lookout to investigate the genetic and molecular basis of age-related common chronic conditions and their interaction with lifestyle and environment in the general population. In CHRIS, the combination of molecular profiling data, such as genomics and metabolomics, together with important baseline clinical and lifestyle data offers vast opportunities for understanding physiological changes that could lead to clinical complications or indicate the prevalence or early onset of diseases together with their molecular underpinnings. Where disease-focused studies often have a clear hypothesis that dictates the necessary statistical analyses, population-based cohorts such as CHRIS are more versatile and allow both testing existing hypotheses as well as generating new hypotheses that arise from statistically significant associations of the available data. Ideally, this type of explorative analysis is open to biomedical researchers that do not necessarily have experience with data analysis or machine learning. Network-based approaches are ideally suited for studying heterogeneous biomedical data, giving rise to the field of network medicine. However, network medicine techniques have so far mainly been used in the context of studies focusing on individual diseases. Network-based platforms for the explorative analysis of population-based cohort data do not exist. In DyHealthNet, we will close this gap and develop a network-based data analysis platform, which will allow to integrate heterogeneous data and support explorative data analytics over dynamically generated subsets of the CHRIS study data. To fully leverage the potential of the available multi-level data, the DyHealthNet platform combines (1) data integration using standardized medical information models (HL7 FHIR), (2) innovative index structures for scalable dynamic analysis, (3) machine learning, and (4) visual analytics. DyHealthNet will render the CHRIS population cohort data accessible for state-of-the-art privacy-preserving, network-based data analysis. DyHealthNet will hence enable mining of context-specific pathomechanisms for precision medicine, and will serve as a blueprint for dynamic explorative analysis of multi-level cohort data worldwide.