Computational Models for Clinical Applications in Personalized Medicine - Guidelines and Recommendations for Data Integration and Model Validation
Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
Standard
Computational Models for Clinical Applications in Personalized Medicine - Guidelines and Recommendations for Data Integration and Model Validation. / Collin, Catherine Bjerre; Gebhardt, Tom; Golebiewski, Martin; Karaderi, Tugce; Hillemanns, Maximilian; Khan, Faiz Muhammad; Salehzadeh-Yazdi, Ali; Kirschner, Marc; Krobitsch, Sylvia; Kuepfer, Lars.
I: Journal of Personalized Medicine, Bind 12, Nr. 2, 166, 2022.Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
Harvard
APA
Vancouver
Author
Bibtex
}
RIS
TY - JOUR
T1 - Computational Models for Clinical Applications in Personalized Medicine - Guidelines and Recommendations for Data Integration and Model Validation
AU - Collin, Catherine Bjerre
AU - Gebhardt, Tom
AU - Golebiewski, Martin
AU - Karaderi, Tugce
AU - Hillemanns, Maximilian
AU - Khan, Faiz Muhammad
AU - Salehzadeh-Yazdi, Ali
AU - Kirschner, Marc
AU - Krobitsch, Sylvia
AU - Kuepfer, Lars
N1 - Publisher Copyright: © 2022 by the authors.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - Clinical translation
KW - Computational models
KW - Data integration
KW - Ethical and legal requirements
KW - Guidelines and recommendations
KW - Model validation
KW - Personalized medicine
U2 - 10.3390/jpm12020166
DO - 10.3390/jpm12020166
M3 - Journal article
C2 - 35207655
AN - SCOPUS:85124959672
VL - 12
JO - Journal of Personalized Medicine
JF - Journal of Personalized Medicine
SN - 2075-4426
IS - 2
M1 - 166
ER -
ID: 299403091