Abstract
Population-based epidemiological cohort studies such as the Cooperative Health Research in South Tyrol (CHRIS) study provide a comprehensive overview of the general population’s health state, effectively capturing the standard physiological range and a spectrum of pathological states. Integrating molecular profiling data with clinical and lifestyle metadata presents significant potential for elucidating physiological changes and underlying mechanisms associated with early disease onset. However, even as the number of cohort studies increases and ever more sophisticated data are collected, fruitful exploration and analysis of these datasets across multiple dimensions remains challenging, even for expert data scientists. Network-based approaches are well-suited for this purpose, integrating multi-level biomedical data to build knowledge graphs that capture system-level and functionally relevant interactions.
In response to these advancements and challenges, we are developing a platform for the dynamic exploration of population cohorts through multi-level network medicine approaches. The platform, DyHealthNet, integrates heterogeneous molecular and baseline metadata into a unified multi-omics network framework to elucidate complex associations among genetic variants, metabolites, and other study-specific features. It supports both global exploratory analyses across the entire cohort data and dynamic computation of associations within user-defined population subsets, enabling the identification of subgroup-specific molecular mechanisms. To enhance biological interpretation, the cohort data is enriched through external knowledge sources, such as NeDRex and HPO. The platform is designed to be modular, configurable via cohort-specific configurations, and easily deployable. Thus, DyHealthNet makes disease association mining and explorative analysis of population cohort data more broadly accessible, even to researchers without programming expertise.