Kristel Van Steen1,2
1BIO3 Systems Genetics, GIGA-R, Université de Liège, 4000 Liège, Belgium
2BIO3 Systems Medicine, Department of Human Genetics, KU Leuven, 3000 Leuven, Belgium
kristel.vansteen [at] uliege.be
Abstract
Big Data offers opportunities in health care to refine individuals’ characterization and thus complement traditional precision medicine approaches toward individual-targeted prevention, diagnosis and treatment management. Not surprisingly, network theory plays a vital role in modelling Big Data: the higher the number of measurements, the higher the number of potential relationships or dependencies among them. Recent developments have shown the complementary value of personalizing population-based networks for individuals (Menche et al. 2017, Dimitrakopoulos et al. 2018) or deriving individual-specific networks via populations of cells (Gosak et al. 2018, Li et al. 2023).
Individual-specific networks do not necessarily require repeated measurements over time or in space. Reverse-engineered individual-specific networks (Kuijjer et al. 2019) from an aggregate network (hereafter referred to as ISNs) allow for investigating the impact of individual-level network wirings, paths or connectivity on medical decision-making in the individual’s interest. Wondering about the utility of these ISNs, we illustrate by example from microbiome and gene co-expression experiments how ISNs give complementary insights in dynamic network biomarker identification and can reveal (genetic modifiers of) co-eQTLs as direct or indirect regulators of gene co-expression.
Keywords: individual-specific networks, precision medicine, precision public health, Big Data science
Acknowledgement: This work received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreements N° 813533 (mlfpm.eu) and N° 860895 (h2020transys.eu). We are grateful to all former and current BIO3 members for inspiring discussions.
