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Author-sourced capture of pathway knowledge in computable form using Biofactoid.
Wong, Jeffrey V; Franz, Max; Siper, Metin Can; Fong, Dylan; Durupinar, Funda; Dallago, Christian; Luna, Augustin; Giorgi, John; Rodchenkov, Igor; Babur, Özgün; Bachman, John A; Gyori, Benjamin M; Demir, Emek; Bader, Gary D; Sander, Chris.
Affiliation
  • Wong JV; The Donnelly Centre, University of Toronto, Toronto, Canada.
  • Franz M; The Donnelly Centre, University of Toronto, Toronto, Canada.
  • Siper MC; Computational Biology Program, Oregon Health and Science University, Portland, United States.
  • Fong D; The Donnelly Centre, University of Toronto, Toronto, Canada.
  • Durupinar F; Computer Science Department, University of Massachusetts Boston, Boston, United States.
  • Dallago C; Department of Cell Biology, Harvard Medical School, Boston, United States.
  • Luna A; Department of Systems Biology, Harvard Medical School, Boston, United States.
  • Giorgi J; Department of Informatics, Technische Universität München, Garching, Germany.
  • Rodchenkov I; Department of Cell Biology, Harvard Medical School, Boston, United States.
  • Babur Ö; Department of Data Sciences, Dana-Farber Cancer Institute, Boston, United States.
  • Bachman JA; Broad Institute, Massachusetts Institute of Technology, Harvard University, Boston, United States.
  • Gyori BM; The Donnelly Centre, University of Toronto, Toronto, Canada.
  • Demir E; The Donnelly Centre, University of Toronto, Toronto, Canada.
  • Bader GD; Computer Science Department, University of Massachusetts Boston, Boston, United States.
  • Sander C; Laboratory of Systems Pharmacology, Harvard Medical School, Boston, United States.
Elife ; 102021 12 03.
Article in En | MEDLINE | ID: mdl-34860157
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.
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Computational Biology / Genomics Language: En Journal: Elife Year: 2021 Document type: Article
Fulltext
Print
XML
PubMed Links
Search on Google

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Computational Biology / Genomics Language: En Journal: Elife Year: 2021 Document type: Article