VACS 2.0: Visual Analysis for Cohort Studies (Visual Analysis of Time-varying High-dimensional Heterogeneous and Incomplete Data with Application to Population-based Studies)

VACS 2.0：队列研究的可视化分析（时变高维异质和不完整数据的可视化分析，应用于基于人群的研究）

• 批准号: 310876543
• 负责人: Professor Dr.-Ing. Lars Linsen
• 金额: --
• 依托单位: Institut für Informatik
• 依托单位国家: 德国
• 项目类别: Research Grants
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/310876543?language=en
• 关键词: VACS; 2.0; Visual; Analysis; Cohort

Clinical practice often focuses on the investigation of one single disease, while the health status of a human is much more complex and may depend on many factors. Recently, cohort studies have been introduced to investigate, in longitudinal studies, the health status of an entire population (the cohort) by capturing health record data, whole-body medical imaging data, personal data including socio-economical circumstances, and even genetic sequencing data. Given this large amount of heterogeneous data, there is a lack of proper tools for its multi-variate analysis. In this project, we propose novel interactive visual analysis methods for testing hypotheses, supporting the generation of new hypotheses, and investigating changes over time. The goal is to allow for the detection of risk or biomarkers and even genetic associations in a multi-variate setting.In the second funding period, the research conducted in the first funding period shall be enhanced in various aspects. We will put a particular focus on the time aspect in multi-dimensional heterogeneous data from longitudinal studies, the analysis of influencing factors, analyzing multi-dimensional heterogeneous data with missing entries, and analyzing sparse high-dimensional data from genome-wide association studies.Moreover, we would like to validate the effectiveness of the proposed analysis methods by performing comparative visual analyses of the multi-dimensional heterogeneous data from different cohort studies.

临床实践通常侧重于对一种单一疾病的研究，而人类的健康状况要复杂得多，可能取决于许多因素。最近，队列研究被引入纵向研究中，通过捕获健康记录数据、全身医学成像数据、包括社会经济状况的个人数据，甚至基因测序数据来调查整个人群（队列）的健康状况。鉴于大量异构数据，缺乏适当的工具进行多变量分析。在这个项目中，我们提出了新颖的交互式视觉分析方法，用于测试假设、支持新假设的生成以及调查随时间的变化。目标是允许在多变量环境中检测风险或生物标志物甚至遗传关联。在第二个资助期中，应在各个方面加强第一个资助期中进行的研究。我们将特别关注纵向研究中多维异质数据的时间方面、影响因素分析、缺失条目的多维异质数据分析以及全基因组关联研究中的稀疏高维数据分析。此外，我们希望通过对多维数据进行比较可视化分析来验证所提出的分析方法的有效性。 来自不同队列研究的异质数据。