The DOI matches its Crossref record; 85 of the 85 references it cites were found in a registry or an index.
What is precision medicine?
Abstract
The term “precision medicine” has become very popular over recent years, fuelled by scientific as well as political perspectives. Despite its popularity, its exact meaning, and how it is different from other popular terms such as “stratified medicine”, “targeted therapy” or “deep phenotyping” remains unclear. Commonly applied definitions focus on the stratification of patients, sometimes referred to as a novel taxonomy, and this is derived using large-scale data including clinical, lifestyle, genetic and further biomarker information, thus going beyond the classical “signs-and-symptoms” approach. While these aspects are relevant, this description leaves open a number of questions. For example, when does precision medicine begin? In which way does the stratification of patients translate into better healthcare? And can precision medicine be viewed as the end-point of a novel stratification of patients, as implied, or is it rather a greater whole? To clarify this, the aim of this paper is to provide a more comprehensive definition that focuses on precision medicine as a process. It will be shown that this proposed framework incorporates the derivation of novel taxonomies and their role in healthcare as part of the cycle, but also covers related terms.
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Cites
The Elements of Statistical Learning
Hastie, T., Tibshirani, R., & Friedman, J. (2009) · Springer Series in Statistics · doi:10.1007/978-0-387-84858-7 · Cited by 25,970
The Elements of Statistical Learning
Hastie, T., Friedman, J., & Tibshirani, R. (2001) · Springer Series in Statistics · doi:10.1007/978-0-387-21606-5 · Cited by 24,162
Everitt, B. J., Landau, S., & Leese, M. (1974) · Cited by 9,168
Harrell (2015) · Springer Series in Statistics · doi:10.1007/978-3-319-19425-7 · Cited by 6,878
Missing data analysis: making it work in the real world
Graham, J. W. (2009) · Annual Review of Psychology · doi:10.1146/annurev.psych.58.110405.085530 · Cited by 6,163
Moons, K. G. M., Altman, D. G., Reitsma, J. B., Ioannidis, J. P. A., Macaskill, P., Steyerberg, E. W., Vickers, A. J., Ransohoff, D. F., & Collins, G. S. (2015) · Annals of Internal Medicine · doi:10.7326/m14-0698 · Cited by 5,147
Anaplastic Lymphoma Kinase Inhibition in Non–Small-Cell Lung Cancer
Kwak, E. L., Bang, Y.-J., Camidge, D. R., Shaw, A. T., Solomon, B., Maki, R. G., Ou, S.-H. I., Dezube, B. J., Jänne, P. A., Costa, D. B., et al. (2010) · New England Journal of Medicine · doi:10.1056/nejmoa1006448 · Cited by 4,446
(2005) · American Journal of Respiratory and Critical Care Medicine · doi:10.1164/rccm.200406-710st · Cited by 3,203
Big data analytics in healthcare: promise and potential
Raghupathi, W., & Raghupathi, V. (2014) · Health Information Science and Systems · doi:10.1186/2047-2501-2-3 · Cited by 3,034
Chen, M., Mao, S., & Liu, Y. (2014) · Mobile Networks and Applications · doi:10.1007/s11036-013-0489-0 · Cited by 2,913
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A review on longitudinal data analysis with random forest
Hu, J., & Szymczak, S. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbad002 · Cited by 678
Drug-microbiota interactions: an emerging priority for precision medicine
Zhao, Q., Chen, Y., Huang, W., Zhou, H., & Zhang, W. (2023) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-023-01619-w · Cited by 268
Multi-modality artificial intelligence in digital pathology
Qiao, Y., Zhao, L., Luo, C., Luo, Y., Wu, Y., Li, S., Bu, D., & Zhao, Y. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac367 · Cited by 44
Pépin, J. L., Bailly, S., & Tamisier, R. (2018) · Thorax · doi:10.1136/thoraxjnl-2017-210943 · Cited by 28
On Utility and Privacy in Synthetic Genomic Data
Oprisanu, B., Ganev, G., & De Cristofaro, E. (2022) · Proceedings 2022 Network and Distributed System Security Symposium · doi:10.14722/ndss.2022.24092 · Cited by 23
Metadata
- Authors
- König, I. R., Fuchs, O., Hansen, G., von Mutius, E., & Kopp, M. V.
- Venue
- European Respiratory Journal
- Publisher
- European Respiratory Society (ERS)
- Published
- 2017-10
- Volume
- 50
- Issue
- 4
- Pages
- 1700391
- ISSN
- 0903-1936 (ISSN-L)
- 1399-3003
- OpenAlex
- W2766525247
- Cited by
- 572
- References
- 85
- FWCI
- 22.86
- Type
- journal article
- Language
- en
- Open access
- closed
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- König, I. R., Fuchs, O., Hansen, G., von Mutius, E., & Kopp, M. V. (2017). What is precision medicine? European Respiratory Journal. https://doi.org/10.1183/13993003.00391-2017
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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