Abstract
There is an increasing interest in the development of effective early detection and intervention strategies in severe mental illness (SMI). Ideally, these efforts should lead to the delineation of accurate staging models of SMI enabling personalized interventions. It is plausible that big data approaches will be instrumental in describing the developmental trajectories of SMI by facilitating the incorporation of data from multiple sources, including those pertaining to the biological make-up of affected subjects. In this review, we first aimed to offer a perspective on how big data are helping the delineation of personalized approaches in SMI, and, second, to offer a quantitative synthesis of big data approaches in metabolomics of SMI. We finally described future directions of this research area.
| Original language | English |
|---|---|
| Pages (from-to) | 75-90 |
| Number of pages | 16 |
| Journal | Personalized Medicine |
| Volume | 18 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - Jan 2021 |
Bibliographical note
Funding Information:This work has received funding from the European Union’s Horizon 2020 Research and Innovation Program under the Marie Sklodowska-Curie CAPICE Project grant agreement number 721567. H Rajula is a PhD student involved in this project. The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed. No writing assistance was utilized in the production of this manuscript.
Publisher Copyright:
© 2021
ASJC Scopus Subject Areas
- Molecular Medicine
- Pharmacology
PubMed: MeSH publication types
- Journal Article
- Research Support, Non-U.S. Gov't
- Review
