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image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao British Journal of P...arrow_drop_down
image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
British Journal of Pharmacology
Article . 2025 . Peer-reviewed
License: Wiley Online Library User Agreement
Data sources: Crossref
https://doi.org/10.22541/au.17...
Article . 2025 . Peer-reviewed
Data sources: Crossref
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Network‐based precision medicine and systems pharmacology

Authors: Arvind K, Pandey; Susan Dina, Ghiassian; Joseph, Loscalzo;

Network‐based precision medicine and systems pharmacology

Abstract

The growth in detailed multi‐omic profiling has created new opportunities to tailor clinical care and therapy to patient‐level variations in disease phenotype. However, efforts towards precision medicine and personalised therapeutics are hampered by limitations in identifying biologically relevant signals that correlate with and underlie disease activity and therapeutic response from these growing arrays of data. These complexities are accentuated further when attempting to translate the new insights in disease pathobiology into new drug targets for treatment. Additionally, understanding how best to reposition existing drugs in the context of new data on disease pathogenesis remains a challenge. Network medicine provides one approach to comprehend these large data sets and identify better the key molecular and phenotypic signals that can function as disease and treatment biomarkers and that can be targeted for therapy. In this review, we discuss basic concepts in the application of network science to biological systems and then build on these concepts to discuss network‐based approaches for identifying novel disease biomarkers, elucidating new drug targets and repositioning existing drugs for new indications. LINKED ARTICLES This article is part of a themed issue Network Medicine and Systems Pharmacology. To view the other articles in this section visit http://onlinelibrary.wiley.com/doi/10.1111/bph.v183.8/issuetoc

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
3
Top 10%
Average
Average
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