Author-sourced capture of pathway knowledge in computable form using Biofactoid
- University of Toronto, Canada
- Oregon Health and Science University, United States
- University of Massachusetts Boston, United States
- Technische Universität München, Germany
- Dana-Farber Cancer Institute, United States
- Harvard University, United States
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
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.
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.
Metrics
-
- 1,987
- views
-
- 115
- downloads
-
- 13
- citations
Views, downloads and citations are aggregated across all versions of this paper published by eLife.
Download links
A two-part list of links to download the article, or parts of the article, in various formats.
Downloads (link to download the article as PDF)
Open citations (links to open the citations from this article in various online reference manager services)
Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)
Author-sourced capture of pathway knowledge in computable form using Biofactoid
eLife 10:e68292.
https://doi.org/10.7554/eLife.68292
Further reading
-
- Computational and Systems Biology
- Neuroscience
Audiovisual information reaches the brain via both sustained and transient input channels, representing signals’ intensity over time or changes thereof, respectively. To date, it is unclear to what extent transient and sustained input channels contribute to the combined percept obtained through multisensory integration. Based on the results of two novel psychophysical experiments, here we demonstrate the importance of the transient (instead of the sustained) channel for the integration of audiovisual signals. To account for the present results, we developed a biologically inspired, general-purpose model for multisensory integration, the multisensory correlation detectors, which combines correlated input from unimodal transient channels. Besides accounting for the results of our psychophysical experiments, this model could quantitatively replicate several recent findings in multisensory research, as tested against a large collection of published datasets. In particular, the model could simultaneously account for the perceived timing of audiovisual events, multisensory facilitation in detection tasks, causality judgments, and optimal integration. This study demonstrates that several phenomena in multisensory research that were previously considered unrelated, all stem from the integration of correlated input from unimodal transient channels.
-
- Computational and Systems Biology
Live-cell microscopy routinely provides massive amounts of time-lapse images of complex cellular systems under various physiological or therapeutic conditions. However, this wealth of data remains difficult to interpret in terms of causal effects. Here, we describe CausalXtract, a flexible computational pipeline that discovers causal and possibly time-lagged effects from morphodynamic features and cell–cell interactions in live-cell imaging data. CausalXtract methodology combines network-based and information-based frameworks, which is shown to discover causal effects overlooked by classical Granger and Schreiber causality approaches. We showcase the use of CausalXtract to uncover novel causal effects in a tumor-on-chip cellular ecosystem under therapeutically relevant conditions. In particular, we find that cancer-associated fibroblasts directly inhibit cancer cell apoptosis, independently from anticancer treatment. CausalXtract uncovers also multiple antagonistic effects at different time delays. Hence, CausalXtract provides a unique computational tool to interpret live-cell imaging data for a range of fundamental and translational research applications.
