Akira Funahashi

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About Akira Funahashi:

No description specified

SEEK ID: https://fairdomhub.org/people/1561

Location: Japan

ORCID: https://orcid.org/0000-0003-0605-239X

Joined: 9th Apr 2020

Expertise: Systems Biology, SBML, Java, Python, Machine Learning, Mathematical modelling, SBGN, Curation

Tools: CellDesigner, SBML, SBGN

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Project positions

Other Projects: COVID-19 Disease Map (Keio University)

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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

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: Severe acute respiratory syndrome coronavirus 2, Homo sapiens

Keio University

Country: Japan

City: Yokohama

Web page: https://www.keio.ac.jp/en/

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Nsp9 interactions (COVID-19 Disease Map)

COVID-19 Disease Map

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A diagram of Nsp9 interactions.

Creators: Noriko Hiroi, Yusuke Hiki, Takahiro G. Yamada, Akira Funahashi

Submitter: Marek Ostaszewski

Model type: Graphical model

Model format: SBML

Environment: Not specified

Organism: Homo sapiens

Investigations: No Investigations

Studies: No Studies

Assays: No Assays

External Link

Created: 20th May 2020 at 17:12, Last updated: 19th Aug 2020 at 11:30

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