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

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  • Catherine Bjerre Collin
  • Tom Gebhardt
  • Martin Golebiewski
  • Karaderi, Tugce
  • Maximilian Hillemanns
  • Faiz Muhammad Khan
  • Ali Salehzadeh-Yazdi
  • Marc Kirschner
  • Sylvia Krobitsch
  • Lars Kuepfer

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.

Original languageEnglish
Article number166
JournalJournal of Personalized Medicine
Volume12
Issue number2
Number of pages24
ISSN2075-4426
DOIs
Publication statusPublished - 2022

Bibliographical note

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© 2022 by the authors.

    Research areas

  • Clinical translation, Computational models, Data integration, Ethical and legal requirements, Guidelines and recommendations, Model validation, Personalized medicine

ID: 299403091