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

Sepideh Sadegh, James Skelton, Elisa Anastasi, Andreas Maier, Klaudia Adamowicz, Anna Möller, Nils M. Kriege, Jaanika Kronberg, Toomas Haller, Tim Kacprowski, Anil Wipat, Jan Baumbach, David B. Blumenthal

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

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.

Original languageEnglish
Article number1662
JournalNature Communications
Volume14
Issue number1
DOIs
StatePublished - Dec 2023
Externally publishedYes

Fingerprint

Dive into the research topics of 'Lacking mechanistic disease definitions and corresponding association data hamper progress in network medicine and beyond'. Together they form a unique fingerprint.

Cite this