COVID19 Disease Map, a computational knowledge repository of virus-host interaction mechanisms.

Détails

ID Serval
serval:BIB_1A41D5739AA2
Type
Article: article d'un périodique ou d'un magazine.
Collection
Publications
Institution
Titre
COVID19 Disease Map, a computational knowledge repository of virus-host interaction mechanisms.
Périodique
Molecular systems biology
Auteur⸱e⸱s
Ostaszewski M., Niarakis A., Mazein A., Kuperstein I., Phair R., Orta-Resendiz A., Singh V., Aghamiri S.S., Acencio M.L., Glaab E., Ruepp A., Fobo G., Montrone C., Brauner B., Frishman G., Monraz Gómez L.C., Somers J., Hoch M., Kumar Gupta S., Scheel J., Borlinghaus H., Czauderna T., Schreiber F., Montagud A., Ponce de Leon M., Funahashi A., Hiki Y., Hiroi N., Yamada T.G., Dräger A., Renz A., Naveez M., Bocskei Z., Messina F., Börnigen D., Fergusson L., Conti M., Rameil M., Nakonecnij V., Vanhoefer J., Schmiester L., Wang M., Ackerman E.E., Shoemaker J.E., Zucker J., Oxford K., Teuton J., Kocakaya E., Summak G.Y., Hanspers K., Kutmon M., Coort S., Eijssen L., Ehrhart F., Rex DAB, Slenter D., Martens M., Pham N., Haw R., Jassal B., Matthews L., Orlic-Milacic M., Senff Ribeiro A., Rothfels K., Shamovsky V., Stephan R., Sevilla C., Varusai T., Ravel J.M., Fraser R., Ortseifen V., Marchesi S., Gawron P., Smula E., Heirendt L., Satagopam V., Wu G., Riutta A., Golebiewski M., Owen S., Goble C., Hu X., Overall R.W., Maier D., Bauch A., Gyori B.M., Bachman J.A., Vega C., Grouès V., Vazquez M., Porras P., Licata L., Iannuccelli M., Sacco F., Nesterova A., Yuryev A., de Waard A., Turei D., Luna A., Babur O., Soliman S., Valdeolivas A., Esteban-Medina M., Peña-Chilet M., Rian K., Helikar T., Puniya B.L., Modos D., Treveil A., Olbei M., De Meulder B., Ballereau S., Dugourd A., Naldi A., Noël V., Calzone L., Sander C., Demir E., Korcsmaros T., Freeman T.C., Augé F., Beckmann J.S., Hasenauer J., Wolkenhauer O., Wilighagen E.L., Pico A.R., Evelo C.T., Gillespie M.E., Stein L.D., Hermjakob H., D'Eustachio P., Saez-Rodriguez J., Dopazo J., Valencia A., Kitano H., Barillot E., Auffray C., Balling R., Schneider R.
Collaborateur⸱rice⸱s
COVID-19 Disease Map Community
ISSN
1744-4292 (Electronic)
ISSN-L
1744-4292
Statut éditorial
Publié
Date de publication
10/2021
Peer-reviewed
Oui
Volume
17
Numéro
10
Pages
e10387
Langue
anglais
Notes
Publication types: Journal Article
Publication Status: ppublish
Résumé
We need to effectively combine the knowledge from surging literature with complex datasets to propose mechanistic models of SARS-CoV-2 infection, improving data interpretation and predicting key targets of intervention. Here, we describe a large-scale community effort to build an open access, interoperable and computable repository of COVID-19 molecular mechanisms. The COVID-19 Disease Map (C19DMap) is a graphical, interactive representation of disease-relevant molecular mechanisms linking many knowledge sources. Notably, it is a computational resource for graph-based analyses and disease modelling. To this end, we established a framework of tools, platforms and guidelines necessary for a multifaceted community of biocurators, domain experts, bioinformaticians and computational biologists. The diagrams of the C19DMap, curated from the literature, are integrated with relevant interaction and text mining databases. We demonstrate the application of network analysis and modelling approaches by concrete examples to highlight new testable hypotheses. This framework helps to find signatures of SARS-CoV-2 predisposition, treatment response or prioritisation of drug candidates. Such an approach may help deal with new waves of COVID-19 or similar pandemics in the long-term perspective.
Mots-clé
computable knowledge repository, large-scale biocuration, omics data analysis, open access community effort, systems biomedicine
Pubmed
Open Access
Oui
Création de la notice
25/10/2021 9:27
Dernière modification de la notice
04/02/2022 7:35
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