The future development of personalized medicine depends on a vast exchange of data from different sources, as well as harmonized integrative analysis of large-scale clinical health and sample data. Computational-modelling approaches play a key role in the analysis of the underlying molecular processes and pathways that characterize human biology, but they also lead to a more profound understanding of the mechanisms and factors that drive diseases; hence, they allow personalized treatment strategies that are guided by central clinical questions. However, despite the growing popularity of computational-modelling approaches in different stakeholder communities, there are still many hurdles to overcome for their clinical routine implementation in the future. Especially the integration of heterogeneous data from multiple sources and types are challenging tasks that require clear guidelines that also have to comply with high ethical and legal standards. Here, we discuss the most relevant computational models for personalized medicine in detail that can be considered as best-practice guidelines for application in clinical care. We define specific challenges and provide applicable guidelines and recommendations for study design, data acquisition, and operation as well as for model validation and clinical translation and other research areas.
SEEK ID: https://publications.h-its.org/publications/1562
PubMed ID: 35207655
Projects: Scientific Databases and Visualisation
Publication type: Journal
Journal: J Pers Med
Citation: J Pers Med. 2022 Jan 26;12(2):166. doi: 10.3390/jpm12020166.
Date Published: 26th Jan 2022
Registered Mode: by PubMed ID
Created: 23rd Jan 2023 at 10:50
Last updated: 24th May 2023 at 15:15