A data driven approach reveals disease similarity on a molecular level.
Computer science
Information theory
Journal
NPJ systems biology and applications
ISSN: 2056-7189
Titre abrégé: NPJ Syst Biol Appl
Pays: England
ID NLM: 101677786
Informations de publication
Date de publication:
2019
2019
Historique:
received:
18
03
2019
accepted:
26
09
2019
entrez:
1
11
2019
pubmed:
2
11
2019
medline:
28
4
2020
Statut:
epublish
Résumé
Could there be unexpected similarities between different studies, diseases, or treatments, on a molecular level due to common biological mechanisms involved? To answer this question, we develop a method for computing similarities between empirical, statistical distributions of high-dimensional, low-sample datasets, and apply it on hundreds of -omics studies. The similarities lead to dataset-to-dataset networks visualizing the landscape of a large portion of biological data. Potentially interesting similarities connecting studies of different diseases are assembled in a disease-to-disease network. Exploring it, we discover numerous non-trivial connections between Alzheimer's disease and schizophrenia, asthma and psoriasis, or liver cancer and obesity, to name a few. We then present a method that identifies the molecular quantities and pathways that contribute the most to the identified similarities and could point to novel drug targets or provide biological insights. The proposed method acts as a "statistical telescope" providing a global view of the constellation of biological data; readers can peek through it at: http://datascope.csd.uoc.gr:25000/.
Identifiants
pubmed: 31666984
doi: 10.1038/s41540-019-0117-0
pii: 117
pmc: PMC6814739
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
39Informations de copyright
© The Author(s) 2019.
Déclaration de conflit d'intérêts
Competing interestsThe authors declare no competing interests.
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