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

Research output: Contribution to journal › Journal article › Research › peer-review

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.

In: Journal of Personalized Medicine, Vol. 12, No. 2, 166, 2022.

Research output: Contribution to journal › Journal article › Research › peer-review

Harvard

Collin, CB, Gebhardt, T, Golebiewski, M, Karaderi, T, Hillemanns, M, Khan, FM, Salehzadeh-Yazdi, A, Kirschner, M, Krobitsch, S & Kuepfer, L 2022, 'Computational Models for Clinical Applications in Personalized Medicine - Guidelines and Recommendations for Data Integration and Model Validation', Journal of Personalized Medicine, vol. 12, no. 2, 166. https://doi.org/10.3390/jpm12020166

APA

Collin, C. B., Gebhardt, T., Golebiewski, M., Karaderi, T., Hillemanns, M., Khan, F. M., Salehzadeh-Yazdi, A., Kirschner, M., Krobitsch, S., & Kuepfer, L. (2022). Computational Models for Clinical Applications in Personalized Medicine - Guidelines and Recommendations for Data Integration and Model Validation. Journal of Personalized Medicine, 12(2), [166]. https://doi.org/10.3390/jpm12020166

Vancouver

Collin CB, Gebhardt T, Golebiewski M, Karaderi T, Hillemanns M, Khan FM et al. Computational Models for Clinical Applications in Personalized Medicine - Guidelines and Recommendations for Data Integration and Model Validation. Journal of Personalized Medicine. 2022;12(2). 166. https://doi.org/10.3390/jpm12020166

Author

Collin, Catherine Bjerre ; Gebhardt, Tom ; Golebiewski, Martin ; Karaderi, Tugce ; Hillemanns, Maximilian ; Khan, Faiz Muhammad ; Salehzadeh-Yazdi, Ali ; Kirschner, Marc ; Krobitsch, Sylvia ; Kuepfer, Lars. / Computational Models for Clinical Applications in Personalized Medicine - Guidelines and Recommendations for Data Integration and Model Validation. In: Journal of Personalized Medicine. 2022 ; Vol. 12, No. 2.

Bibtex

@article{60ef29c5c033487cac0a445d27c59c5b,
title = "Computational Models for Clinical Applications in Personalized Medicine - Guidelines and Recommendations for Data Integration and Model Validation",
abstract = "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.",
keywords = "Clinical translation, Computational models, Data integration, Ethical and legal requirements, Guidelines and recommendations, Model validation, Personalized medicine",
author = "Collin, {Catherine Bjerre} and Tom Gebhardt and Martin Golebiewski and Tugce Karaderi and Maximilian Hillemanns and Khan, {Faiz Muhammad} and Ali Salehzadeh-Yazdi and Marc Kirschner and Sylvia Krobitsch and Lars Kuepfer",
note = "Publisher Copyright: {\textcopyright} 2022 by the authors.",
year = "2022",
doi = "10.3390/jpm12020166",
language = "English",
volume = "12",
journal = "Journal of Personalized Medicine",
issn = "2075-4426",
publisher = "Multidisciplinary Digital Publishing Institute (MDPI)",
number = "2",

}

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