Overview: Data Generation Techniques: From Omics to Personalized Approaches and Clinical Care

In efforts to better understand human complex pathologies we are faced with raising numbers of data, from different resources, different experimental models, and different patients. It is acknowledged that one of the gaps is making data available for future research, taking into account the FAIR (findable, reusable, interoperable, and reproducible) principles. On the other hand, it should not be forgotten that data generation techniques are of equal importance. In this section, we discuss a variety of data-based approaches on different multifactorial diseases as use cases. Transcriptomics and other omic technologies hold a great potential not only for improved diagnostics of complex diseases, but also for improved prognosis and treatment optimizations where network enrichment methods can be applied to decipher mechanisms and find disease overlaps on the molecular level. New diagnostic and prognostic biomarkers remain a need where multiomics proved its essentiality. An important part of this section is also the clinical view. Clinicians and other health scientists are faced by challenges in daily practice to better understand and manage patients with the aid of available data. Big data in clinical practice is a big issue, especially in primary care where systems approaches are applied and realized as personalized medicine. One take-home message from this section is focused on patients as a resource of the data: we should not forget the ethical principles and the humanity. A human individual is much more than a collection of data.