Display options
Cite
Wong JV, Franz M, Siper MC, et al. Author-sourced capture of pathway knowledge in computable form using Biofactoid. Elife. 2021;10doi: 10.7554/eLife.68292.
Wong, J. V., Franz, M., Siper, M. C., Fong, D., Durupinar, F., Dallago, C., Luna, A., Giorgi, J. M., Rodchenkov, I., Babur, �. �., Bachman, J. A., Gyori, B., Demir, E., Bader, G. D., & Sander, C. (2021). Author-sourced capture of pathway knowledge in computable form using Biofactoid. eLife, 10. https://doi.org/10.7554/eLife.68292
Wong, Jeffrey V, et al. "Author-sourced capture of pathway knowledge in computable form using Biofactoid." eLife vol. 10 (2021). doi: https://doi.org/10.7554/eLife.68292
Wong JV, Franz M, Siper MC, Fong D, Durupinar F, Dallago C, Luna A, Giorgi JM, Rodchenkov I, Babur Ö, Bachman JA, Gyori B, Demir E, Bader GD, Sander C. Author-sourced capture of pathway knowledge in computable form using Biofactoid. Elife. 2021 Dec 03;10. doi: 10.7554/eLife.68292. Epub 2021 Dec 03. PMID: 34860157.
Copy Download .nbib Format: NLM
Share it on

Elife. 2021 Dec 03;10. doi: 10.7554/eLife.68292. Epub 2021 Dec 03.

Author-sourced capture of pathway knowledge in computable form using Biofactoid.

eLife

Jeffrey V Wong, Max Franz, Metin Can Siper, Dylan Fong, Funda Durupinar, Christian Dallago, Augustin Luna, John M Giorgi, Igor Rodchenkov, Özgün Babur, John A Bachman, Benjamin Gyori, Emek Demir, Gary D Bader, Chris Sander

Affiliations

  1. The Donnelly Centre, University of Toronto, Toronto, Canada.
  2. Computational Biology Program, Oregon Health and Science University, Oregon, United States.
  3. Computer Science Department, University of Massachusetts Boston, Boston, United States.
  4. Department of Informatics, Technische Universität München, Garching, Germany.
  5. Department of Data Sciences, Dana-Farber Cancer Institute, Boston, United States.
  6. Laboratory of Systems Pharmacology, Harvard Medical School, Harvard University, Boston, United States.
  7. Computational Biology Program, Oregon Health and Science University, Portland, United States.
  8. Department of Cell Biology, Harvard Medical School, Harvard University, Boston, United States.

PMID: 34860157 DOI: 10.7554/eLife.68292

Abstract

Making the knowledge contained in scientific papers machine-readable and formally computable would allow researchers to take full advantage of this information by enabling integration with other knowledge sources to support data analysis and interpretation. Here we describe Biofactoid, a web-based platform that allows scientists to specify networks of interactions between genes, their products, and chemical compounds, and then translates this information into a representation suitable for computational analysis, search and discovery. We also report the results of a pilot study to encourage the wide adoption of Biofactoid by the scientific community.

© 2021, Wong et al.

Keywords: computational biology; genetics; genomics; none; systems biology

Conflict of interest statement

JW, MF, MS, DF, FD, CD, AL, JG, IR, ÖB, JB, BG, ED, GB, CS The authors declare that no competing interests exist.

Publication Types

Grant support