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Overview: Data Generation Techniques: From Omics to Personalized Approaches and
Clinical Care
#MMPMIDC7278532
Rozman D
Systems Medicine
2021[]; ? (?): 222-3
PMIDC7278532
show ga
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.
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