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

Author:

Wong Jeffrey V1ORCID,Franz Max1ORCID,Siper Metin Can2ORCID,Fong Dylan1,Durupinar Funda3ORCID,Dallago Christian456ORCID,Luna Augustin478ORCID,Giorgi John1ORCID,Rodchenkov Igor1,Babur Özgün3ORCID,Bachman John A9ORCID,Gyori Benjamin M9ORCID,Demir Emek2ORCID,Bader Gary D1101112ORCID,Sander Chris78ORCID

Affiliation:

1. The Donnelly Centre, University of Toronto

2. Computational Biology Program, Oregon Health and Science University

3. Computer Science Department, University of Massachusetts Boston

4. Department of Cell Biology, Harvard Medical School

5. Department of Systems Biology, Harvard Medical School

6. Department of Informatics, Technische Universität München

7. Department of Data Sciences, Dana-Farber Cancer Institute

8. Broad Institute, Massachusetts Institute of Technology, Harvard University

9. Laboratory of Systems Pharmacology, Harvard Medical School

10. Department of Computer Science, Department of Molecular Genetics, University of Toronto

11. The Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital

12. Princess Margaret Cancer Centre, University Health Network

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.

Funder

National Human Genome Research Institute

National Institute of General Medical Sciences

Defense Advanced Research Projects Agency

Publisher

eLife Sciences Publications, Ltd

Subject

General Immunology and Microbiology,General Biochemistry, Genetics and Molecular Biology,General Medicine,General Neuroscience

Reference44 articles.

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4. Growth rates of modern science: a bibliometric analysis based on the number of publications and cited references;Bornmann;Journal of the Association for Information Science and Technology,2015

5. Gene: a gene-centered information resource at NCBI;Brown;Nucleic Acids Research,2015

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