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)

临床实践往往侧重于对单一疾病的研究，而人类的健康状况要复杂得多，可能取决于许多因素。最近，队列研究已经被引入到纵向研究中，通过捕获健康记录数据、全身医学成像数据、包括社会经济情况的个人数据，甚至基因测序数据来调查整个人群（队列）的健康状况。由于这些大量的异构数据，缺乏适当的工具来进行多变量分析。在这个项目中，我们提出了新的交互式视觉分析方法来测试假设，支持新假设的生成，并调查随着时间的推移而发生的变化。在第二个资助期内，我们会在多方面加强第一个资助期内进行的研究。我们将特别关注纵向研究中多维异质数据的时间方面，影响因素的分析，缺失条目的多维异质数据的分析，以及全基因组关联研究中稀疏高维数据的分析。此外，我们希望通过对多个数据集进行比较视觉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多维异质性数据。

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.