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Lacking mechanistic disease definitions and corresponding association data hamper progress in network medicine and beyond

Lookup NU author(s): Dr James Skelton, Dr Elisa Anastasi, Professor Anil Wipat



This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).


© 2023, The Author(s).A long-term objective of network medicine is to replace our current, mainly phenotype-based disease definitions by subtypes of health conditions corresponding to distinct pathomechanisms. For this, molecular and health data are modeled as networks and are mined for pathomechanisms. However, many such studies rely on large-scale disease association data where diseases are annotated using the very phenotype-based disease definitions the network medicine field aims to overcome. This raises the question to which extent the biases mechanistically inadequate disease annotations introduce in disease association data distort the results of studies which use such data for pathomechanism mining. We address this question using global- and local-scale analyses of networks constructed from disease association data of various types. Our results indicate that large-scale disease association data should be used with care for pathomechanism mining and that analyses of such data should be accompanied by close-up analyses of molecular data for well-characterized patient cohorts.

Publication metadata

Author(s): Sadegh S, Skelton J, Anastasi E, Maier A, Adamowicz K, Moller A, Kriege NM, Kronberg J, Haller T, Kacprowski T, Wipat A, Baumbach J, Blumenthal DB

Publication type: Article

Publication status: Published

Journal: Nature Communications

Year: 2023

Volume: 14

Issue: 1

Online publication date: 25/03/2023

Acceptance date: 13/03/2023

Date deposited: 11/04/2023

ISSN (electronic): 2041-1723

Publisher: Nature Research


DOI: 10.1038/s41467-023-37349-4

PubMed id: 36966134


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2014-2020.4.01.15-0012 GENTRANSMED
777111Commission of the European Communities