Interactomen in precisiegezondheid: van populatie tot individu-specifieke netwerken.

Describing a system implies describing its behavior and important control mechanisms that regulate this behavior. Crucial in this process are interactions, which may occur at different levels or scales, and thus network theory and network visualization are increasingly being used to understand biological mechanisms operating in human systems. However, an individual, especially when in poor health, is likely to deviate from the "norm" in human systems. In this project, we wish to develop omics data integrative gene-based networks to enhance PM. Such a network would enable the identification of gene modules that are subject-specific (in network nodes/edges) and comprise multi-layer cellular information. It goes beyond existing work in that genes are considered to be complex multi-omics systems, and that statistical significance is assessed for individual-specific nodes/edges (in contrast to f.i. Menche et al. 2017 and Kuijjer et al. 2018). We aim to achieve our goal by building upon the aforementioned references and our work on gene representations using diffusion kernels and network theory (Fouladi et al. 2018). Personalized gene omics-integrative signatures will primarily be derived by combining genome, transcriptome and/of epigenome data for complex diseases with an inflammatory component.