Titel
Lacking mechanistic disease definitions and corresponding association data hamper progress in network medicine and beyond
Autor*in
Sepideh Sadegh
Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich
James Skelton
School of Computing, Newcastle University
Elisa Anastasi
School of Computing, Newcastle University
... show all
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.
Stichwort
Computer scienceData integrationData miningMolecular medicineTranslational research
Objekt-Typ
Sprache
Englisch [eng]
Persistent identifier
phaidra.univie.ac.at/o:2046415
Erschienen in
Titel
Nature Communications
Band
14
ISSN
2041-1723
Erscheinungsdatum
2023
Publication
Springer Science and Business Media LLC
Fördergeber
Europäische Union (alle Programme) — 777111
Europäische Union (alle Programme) — 2014-2020.4.01.15-0012
Erscheinungsdatum
2023
Zugänglichkeit
Rechte
Rechteangabe
© The Author(s) 2023
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