Tom Freeman

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About Tom Freeman:

No description specified

SEEK ID: https://fairdomhub.org/people/1649

Location: United Kingdom

ORCID: Not specified

Expertise: Modelling

Tools: Not specified

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Disease Maps

The Disease Maps Project is designed as a large-scale community effort. It is a network of groups that work together in order to better understand disease mechanisms. The project exchanges best practices, share information, develop tools to make it easier for all the involved groups to achieve their goals.

Projects: COVID-19 Disease Map

Web page: https://disease-maps.org

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COVID-19 Disease Map

Here we share resources and best practices to develop a disease map for COVID-19. The project is progressing as a broad community-driven effort. We aim to establish a knowledge repository on virus-host interaction mechanisms specific to the SARS-CoV-2. The COVID-19 Disease Map is an assembly of molecular interaction diagrams established based on literature evidence.

Programme: Disease Maps

Public web page: http://doi.org/10.17881/covid19-disease-map

Organisms: Homo sapiens, Severe acute respiratory syndrome coronavirus 2

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University of Edinburgh

Country: United Kingdom

City: Edinburgh

Web page: http://www.ed.ac.uk/

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Virus replication cycle in mEPN/SBGN (COVID-19 Disease Map)

COVID-19 Disease Map

Mechanisms related to COVID-19 virus replication cycle, constructed using the mEPN graphical notation.

Creators: Liam Fergusson, Tom Freeman

Submitter: Marek Ostaszewski

Model type: Graphical model

Model format: Not specified

Environment: Not specified

Organism: Homo sapiens

Investigations: No Investigations

Studies: No Studies

Assays: No Assays

External Link

Created: 8th Jun 2020 at 11:59, Last updated: 8th Jun 2020 at 11:59

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

COVID-19 Disease Map

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Abstract (Expand)

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 …

Authors: M. Ostaszewski, A. Niarakis, A. Mazein, I. Kuperstein, R. Phair, A. Orta-Resendiz, V. Singh, S. S. Aghamiri, M. L. Acencio, E. Glaab, A. Ruepp, G. Fobo, C. Montrone, B. Brauner, G. Frishman, L. C. Monraz Gomez, J. Somers, M. Hoch, S. Kumar Gupta, J. Scheel, H. Borlinghaus, T. Czauderna, F. Schreiber, A. Montagud, M. Ponce de Leon, A. Funahashi, Y. Hiki, N. Hiroi, T. G. Yamada, A. Drager, A. Renz, M. Naveez, Z. Bocskei, F. Messina, D. Bornigen, L. Fergusson, M. Conti, M. Rameil, V. Nakonecnij, J. Vanhoefer, L. Schmiester, M. Wang, E. E. Ackerman, J. E. Shoemaker, J. Zucker, K. Oxford, J. Teuton, E. Kocakaya, G. Y. Summak, K. Hanspers, M. Kutmon, S. Coort, L. Eijssen, F. Ehrhart, D. A. B. Rex, D. Slenter, M. Martens, N. Pham, R. Haw, B. Jassal, L. Matthews, M. Orlic-Milacic, A. Senff Ribeiro, K. Rothfels, V. Shamovsky, R. Stephan, C. Sevilla, T. Varusai, J. M. Ravel, R. Fraser, V. Ortseifen, S. Marchesi, P. Gawron, E. Smula, L. Heirendt, V. Satagopam, G. Wu, A. Riutta, M. Golebiewski, S. Owen, C. Goble, X. Hu, R. W. Overall, D. Maier, A. Bauch, B. M. Gyori, J. A. Bachman, C. Vega, V. Groues, M. Vazquez, P. Porras, L. Licata, M. Iannuccelli, F. Sacco, A. Nesterova, A. Yuryev, A. de Waard, D. Turei, A. Luna, O. Babur, S. Soliman, A. Valdeolivas, M. Esteban-Medina, M. Pena-Chilet, K. Rian, T. Helikar, B. L. Puniya, D. Modos, A. Treveil, M. Olbei, B. De Meulder, S. Ballereau, A. Dugourd, A. Naldi, V. Noel, L. Calzone, C. Sander, E. Demir, T. Korcsmaros, T. C. Freeman, F. Auge, J. S. Beckmann, J. Hasenauer, O. Wolkenhauer, E. L. Wilighagen, A. R. Pico, C. T. Evelo, M. E. Gillespie, L. D. Stein, H. Hermjakob, P. D'Eustachio, J. Saez-Rodriguez, J. Dopazo, A. Valencia, H. Kitano, E. Barillot, C. Auffray, R. Balling, R. Schneider