Computational Models for Clinical Applications in Personalized Medicine—Guidelines and Recommendations for Data Integration and Model Validation

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.Funding: The authors of this article are part of the EU-STANDS4PM consortium (www.eu-stands4pm.eu) that is funded by the European Union Horizon 2020 framework programme of the European Commission under Grant Agreement # 825843. Faiz Muhammad Khan received additional funding from The German Federal Ministry of Education and Research (BMBF) and the SASKit project (FKZ 01ZX1903B). Tugce Karaderi is supported by the Novo Nordisk Foundation Data Science Investigator grant (NNF20OC0062294)