COVID-19 Knowledge Graph: a computable, multi-modal, cause-and-effect knowledge model of COVID-19 pathophysiology
This article has been Reviewed by the following groups
Discuss this preprint
Start a discussion What are Sciety discussions?Listed in
- Evaluated articles (ScreenIT)
Abstract
Summary
The past few weeks have witnessed a worldwide mobilization of the research community in response to the novel coronavirus (COVID-19). This global response has led to a burst of publications on the pathophysiology of the virus, yet without coordinated efforts to organize this knowledge, it can remain hidden away from individual research groups. By extracting and formalizing this knowledge in a structured and computable form, as in the form of a knowledge graph, researchers can readily reason and analyze this information on a much larger scale. Here, we present the COVID-19 Knowledge Graph, an expansive cause-and-effect network constructed from scientific literature on the new coronavirus that aims to provide a comprehensive view of its pathophysiology. To make this resource available to the research community and facilitate its exploration and analysis, we also implemented a web application and released the KG in multiple standard formats.
Availability
The COVID-19 Knowledge Graph is publicly available under CC-0 license at https://github.com/covid19kg and https://bikmi.covid19-knowledgespace.de .
Contact
alpha.tom.kodamullil@scai.fraunhofer.de
Supplementary information
Supplementary data are available online.
Article activity feed
-
SciScore for 10.1101/2020.04.14.040667: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Software and Algorithms Sentences Resources 2.3 Web Application: In order to better aid the exploration and usage of the generated COVID-19 Knowledge Graph, a web application was developed using Biological Knowledge Miner (BiKMi), an in-house software package designed for exploring pathways and molecular interactions within a BEL-derived network. using Biologicalsuggested: NoneThe front-end of the application was constructed using the Python Django web framework, while the back-end of the software is implemented using OrientDB, a multi-model database management system that allows for both relational and graph queries to be made … SciScore for 10.1101/2020.04.14.040667: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Software and Algorithms Sentences Resources 2.3 Web Application: In order to better aid the exploration and usage of the generated COVID-19 Knowledge Graph, a web application was developed using Biological Knowledge Miner (BiKMi), an in-house software package designed for exploring pathways and molecular interactions within a BEL-derived network. using Biologicalsuggested: NoneThe front-end of the application was constructed using the Python Django web framework, while the back-end of the software is implemented using OrientDB, a multi-model database management system that allows for both relational and graph queries to be made against a database via its API (Supplementary Information), which opens the avenue towards systematic comparison of different COVID models. Pythonsuggested: (IPython, RRID:SCR_001658)Djangosuggested: (Django, RRID:SCR_012855)Results from OddPub: Thank you for sharing your code.
Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
-
SciScore for 10.1101/2020.04.14.040667: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement not detected. Randomization not detected. Blinding not detected. Power Analysis not detected. Sex as a biological variable not detected. Table 2: Resources
Software and Algorithms Sentences Resources 2.3 Web ApplicationIn order to better aid the exploration and usage of the generated COVID-19 Knowledge Graph, a web application was developed using Biological Knowledge Miner (BiKMi), an in-house software package designed for exploring pathways and molecular interactions within a BEL-derived network. using Biologicalsuggested: NoneThe front-end of the application was constructed using the Python Django web … SciScore for 10.1101/2020.04.14.040667: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement not detected. Randomization not detected. Blinding not detected. Power Analysis not detected. Sex as a biological variable not detected. Table 2: Resources
Software and Algorithms Sentences Resources 2.3 Web ApplicationIn order to better aid the exploration and usage of the generated COVID-19 Knowledge Graph, a web application was developed using Biological Knowledge Miner (BiKMi), an in-house software package designed for exploring pathways and molecular interactions within a BEL-derived network. using Biologicalsuggested: NoneThe front-end of the application was constructed using the Python Django web framework, while the back-end of the software is implemented using OrientDB, a multi-model database management system that allows for both relational and graph queries to be made against a database via its API (Supplementary Information), which opens the avenue towards systematic comparison of different COVID models. Pythonsuggested: (IPython, SCR_001658)<div style="margin-bottom:8px"> <div><b>Django</b></div> <div>suggested: (Django, <a href="https://scicrunch.org/resources/Any/search?q=SCR_012855">SCR_012855</a>)</div> </div> </td></tr></table>Results from OddPub: Thank you for sharing your code.
About SciScore
SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore is not a substitute for expert review. SciScore checks for the presence and correctness of RRIDs (research resource identifiers) in the manuscript, and detects sentences that appear to be missing RRIDs. SciScore also checks to make sure that rigor criteria are addressed by authors. It does this by detecting sentences that discuss criteria such as blinding or power analysis. SciScore does not guarantee that the rigor criteria that it detects are appropriate for the particular study. Instead it assists authors, editors, and reviewers by drawing attention to sections of the manuscript that contain or should contain various rigor criteria and key resources. For details on the results shown here, please follow this link.
-
