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Clustering of Alzheimer's and Parkinson's disease based on genetic burden of shared molecular mechanisms

 
: Emon, Mohammad Asif; Heinson, Ashley; Wu, Ping; Domingo-Fernández, Daniel; Sood, Meemansa; Vrooman, Henri; Corvol, Christophe; Scordis, Phil; Hofmann-Apitius, Martin; Fröhlich, Holger

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Volltext urn:nbn:de:0011-n-6084913 (3.5 MByte PDF)
MD5 Fingerprint: b5ed76d8b5ed7d50ff9396669da6d749
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Erstellt am: 10.11.2020


Scientific Reports 10 (2020), Art. 19097, 16 S.
ISSN: 2045-2322
European Commission EC
FP7-JTI; 115568; AETIONOMY
Aetionomy - Organising Mechanistic Knowledge about Neurodegenerative Diseases for the Improvement of Drug Development and Therapy
Englisch
Zeitschriftenaufsatz, Elektronische Publikation
Fraunhofer SCAI ()
Drup Repurposing; computational science; drug development; genetics research; translational research

Abstract
One of the visions of precision medicine has been to re-define disease taxonomies based on molecular characteristics rather than on phenotypic evidence. However, achieving this goal is highly challenging, specifically in neurology. Our contribution is a machine-learning based joint molecular subtyping of Alzheimers (AD) and Parkinsons Disease (PD), based on the genetic burden of 15 molecular mechanisms comprising 27 proteins (e.g. APOE) that have been described in both diseases. We demonstrate that our joint AD/PD clustering using a combination of sparse autoencoders and sparse non-negative matrix factorization is reproducible and can be associated with significant differences of AD and PD patient subgroups on a clinical, pathophysiological and molecular level. Hence, clusters are disease-associated. To our knowledge this work is the first demonstration of a mechanism based stratification in the field of neurodegenerative diseases. Overall, we thus see this work as an important step towards a molecular mechanism-based taxonomy of neurological disorders, which could help in developing better targeted therapies in the future by going beyond classical phenotype based disease definitions.

: http://publica.fraunhofer.de/dokumente/N-608491.html