1. Computational and Systems Biology
  2. Genetics and Genomics
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Author-sourced capture of pathway knowledge in computable form using Biofactoid

  1. Jeffrey V Wong  Is a corresponding author
  2. Max Franz
  3. Metin Can Siper
  4. Dylan Fong
  5. Funda Durupinar
  6. Christian Dallago
  7. Augustin Luna
  8. John M Giorgi
  9. Igor Rodchenkov
  10. Özgün Babur
  11. John A Bachman
  12. Benjamin Gyori
  13. Emek Demir  Is a corresponding author
  14. Gary D Bader  Is a corresponding author
  15. Chris Sander  Is a corresponding author
  1. University of Toronto, Canada
  2. Oregon Health and Science University, United States
  3. University of Massachusetts Boston, United States
  4. Technische Universität München, Germany
  5. Dana-Farber Cancer Institute, United States
  6. Harvard University, United States
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Cite this article as: eLife 2021;10:e68292 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.

Data availability

All Biofactoid data are available under the Creative Commons CC0 public domain license. To download the data and code, please refer to the documentation on the Biofactoid GitHub repository (github.com/PathwayCommons/factoid). More information on software availability is available in Materials and methods.

Article and author information

Author details

  1. Jeffrey V Wong

    The Donnelly Centre, University of Toronto, Toronto, Canada
    For correspondence
    jeffvin.wong@utoronto.ca
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-8912-5699
  2. Max Franz

    The Donnelly Centre, University of Toronto, Toronto, Canada
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-0169-0480
  3. Metin Can Siper

    Computational Biology Program, Oregon Health and Science University, Oregon, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-7556-093X
  4. Dylan Fong

    The Donnelly Centre, University of Toronto, Toronto, Canada
    Competing interests
    The authors declare that no competing interests exist.
  5. Funda Durupinar

    Computer Science Department, University of Massachusetts Boston, Boston, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-4915-6642
  6. Christian Dallago

    Department of Informatics, Technische Universität München, Garching, Germany
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-4650-6181
  7. Augustin Luna

    Department of Data Sciences, Dana-Farber Cancer Institute, Boston, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-5709-371X
  8. John M Giorgi

    The Donnelly Centre, University of Toronto, Toronto, Canada
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-9621-5046
  9. Igor Rodchenkov

    The Donnelly Centre, University of Toronto, Toronto, Canada
    Competing interests
    The authors declare that no competing interests exist.
  10. Özgün Babur

    Computer Science Department, University of Massachusetts Boston, Boston, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-0239-5259
  11. John A Bachman

    Laboratory of Systems Pharmacology, Harvard Medical School, Harvard University, Boston, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-6095-2466
  12. Benjamin Gyori

    Laboratory of Systems Pharmacology, Harvard Medical School, Harvard University, Boston, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-9439-5346
  13. Emek Demir

    Computational Biology Program, Oregon Health and Science University, Portland, United States
    For correspondence
    demire@ohsu.edu
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-3663-7113
  14. Gary D Bader

    The Donnelly Centre, University of Toronto, Toronto, Canada
    For correspondence
    gary.bader@utoronto.ca
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-0185-8861
  15. Chris Sander

    Department of Cell Biology, Harvard Medical School, Harvard University, Boston, United States
    For correspondence
    sander.research@gmail.com
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-6059-6270

Funding

National Human Genome Research Institute (U41 HG006623)

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

National Human Genome Research Institute (U41 HG003751)

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

National Human Genome Research Institute (R01 HG009979)

  • Max Franz
  • Gary D Bader

National Institute of General Medical Sciences (P41 GM103504)

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

Defense Advanced Research Projects Agency (Big Mechanism,ARO W911NF-14-C-0119)

  • Metin Can Siper
  • Funda Durupinar
  • Özgün Babur
  • John A Bachman
  • Benjamin Gyori
  • Emek Demir

Defense Advanced Research Projects Agency (Communicating with Computers,ARO W911NF-15-1-054)

  • Metin Can Siper
  • Funda Durupinar
  • Özgün Babur
  • John A Bachman
  • Benjamin Gyori
  • Emek Demir

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Ethics

Human subjects: Participants of user testing provided written consent to volunteer, have their testing sessions recorded and have quotes obtained in the session published.

Reviewing Editor

  1. Helena Pérez Valle, eLife, United Kingdom

Publication history

  1. Preprint posted: March 11, 2021 (view preprint)
  2. Received: March 11, 2021
  3. Accepted: December 2, 2021
  4. Accepted Manuscript published: December 3, 2021 (version 1)
  5. Version of Record published: December 17, 2021 (version 2)

Copyright

© 2021, Wong et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

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Further reading

    1. Computational and Systems Biology
    2. Neuroscience
    Ling-Qi Zhang et al.
    Research Article

    We developed an image-computable observer model of the initial visual encoding that operates on natural image input, based on the framework of Bayesian image reconstruction from the excitations of the retinal cone mosaic. Our model extends previous work on ideal observer analysis and evaluation of performance beyond psychophysical discrimination, takes into account the statistical regularities of the visual environment, and provides a unifying framework for answering a wide range of questions regarding the visual front end. Using the error in the reconstructions as a metric, we analyzed variations of the number of different photoreceptor types on human retina as an optimal design problem. In addition, the reconstructions allow both visualization and quantification of information loss due to physiological optics and cone mosaic sampling, and how these vary with eccentricity. Furthermore, in simulations of color deficiencies and interferometric experiments, we found that the reconstructed images provide a reasonable proxy for modeling subjects' percepts. Lastly, we used the reconstruction-based observer for the analysis of psychophysical threshold, and found notable interactions between spatial frequency and chromatic direction in the resulting spatial contrast sensitivity function. Our method is widely applicable to experiments and applications in which the initial visual encoding plays an important role.

    1. Computational and Systems Biology
    2. Medicine
    Xuan Xu et al.
    Research Article Updated

    Background:

    Potential therapy and confounding factors including typical co‐administered medications, patient’s disease states, disease prevalence, patient demographics, medical histories, and reasons for prescribing a drug often are incomplete, conflicting, missing, or uncharacterized in spontaneous adverse drug event (ADE) reporting systems. These missing or incomplete features can affect and limit the application of quantitative methods in pharmacovigilance for meta-analyses of data during randomized clinical trials.

    Methods:

    Data from patients with hypertension were retrieved and integrated from the FDA Adverse Event Reporting System; 134 antihypertensive drugs out of 1131 drugs were filtered and then evaluated using the empirical Bayes geometric mean (EBGM) of the posterior distribution to build ADE-drug profiles with an emphasis on the pulmonary ADEs. Afterward, the graphical least absolute shrinkage and selection operator (GLASSO) captured drug associations based on pulmonary ADEs by correcting hidden factors and confounder misclassification. Selected drugs were then compared using the Friedman test in drug classes and clusters obtained from GLASSO.

    Results:

    Following multiple filtering stages to exclude insignificant and noise-driven reports, we found that drugs from antihypertensives agents, urologicals, and antithrombotic agents (macitentan, bosentan, epoprostenol, selexipag, sildenafil, tadalafil, and beraprost) form a similar class with a significantly higher incidence of pulmonary ADEs. Macitentan and bosentan were associated with 64% and 56% of pulmonary ADEs, respectively. Because these two medications are prescribed in diseases affecting pulmonary function and may be likely to emerge among the highest reported pulmonary ADEs, in fact, they serve to validate the methods utilized here. Conversely, doxazosin and rilmenidine were found to have the least pulmonary ADEs in selected drugs from hypertension patients. Nifedipine and candesartan were also found by signal detection methods to form a drug cluster, shown by several studies an effective combination of these drugs on lowering blood pressure and appeared an improved side effect profile in comparison with single-agent monotherapy.

    Conclusions:

    We consider pulmonary ADE profiles in multiple long-standing groups of therapeutics including antihypertensive agents, antithrombotic agents, beta-blocking agents, calcium channel blockers, or agents acting on the renin-angiotensin system, in patients with hypertension associated with high risk for coronavirus disease 2019 (COVID-19). We found that several individual drugs have significant differences between their drug classes and compared to other drug classes. For instance, macitentan and bosentan from endothelin receptor antagonists show major concern while doxazosin and rilmenidine exhibited the least pulmonary ADEs compared to the outcomes of other drugs. Using techniques in this study, we assessed and confirmed the hypothesis that drugs from the same drug class could have very different pulmonary ADE profiles affecting outcomes in acute respiratory illness.

    Funding:

    GJW and MJD accepted funding from BioNexus KC for funding on this project, but BioNexus KC had no direct role in this article.