KG-COVID-19: A Framework to Produce Customized Knowledge Graphs for COVID-19 Response

Reese, Justin; Unni, Deepak; Callahan, Tiffany J; Cappelletti, Luca; Ravanmehr, Vida; Carbon, Seth; Fontana, Tommaso; Blau, Hannah; Matentzoglu, Nicolas; Harris, Nomi L.; Muñoz-Torres, Monica; Robinson, Peter N.; Joachimiak, Marcin P.; Mungall, Christopher J.

doi:10.2139/ssrn.3681978

Cited by 11 publications

(24 citation statements)

References 8 publications

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“…Name # Description Format Method Biolink [17,18] 1 Biolink Metamodel (semantic layer) TTL RBM ChEMBL [24,61] 1 EMBL Chemogenomic Database SQL D2J DGIdb [62] 1 Drug Gene Interaction Database TSV D2J DisGeNET [63] 1 Disease-Gene Associations TSV D2J DrugBank [23] 1 Pharmaceutical Knowledge Base XML D2J DrugCentral [64] 1 Online drug Compendium SQL D2J Ensembl Gene [65] 1 Ensembl Human Gene annotations JSON D2J EFO [57] 1 Experimental Factor Ontology OWL RBM GO [66,67] 1 Gene Ontology annotations TSV D2J HMDB [68][69][70][71] 1 Human Metabolite Database XML D2J IntAct [72,73] 1 IntAct Molecular Interaction Database TSV D2J Jensen Lab Diseases [74] 1 Gene to Diseases Dataset TSV D2J KEGG [21,75,76] 1 Kyoto Encyclopedia of Genes and Genomes API D2J miRBase [77][78][79][80][81] 1 MicroRNAs Dataset DAT D2J NCBI Gene [82] 1 NCBI Human Gene annotations TSV D2J OBO Foundry 21 OBO Foundry Ontologies (Table S1) OWL RBM Orphanet [83] 1 Orphanet Rare Disease Ontology OWL RBM PathBank [84][85][86] 1 Wishart Lab Pathway Databases XML D2J Reactome [87] 1 Pathway Database SQL D2J SemMedDB [29] 1 Semantic Medline Database SQL D2J SMPDB [26,27] 1 Small Molecule Pathway Database CSV D2J UMLS [88] 26 Unified Medical Language System (Table 3) TTL RBM UniChem…”

Section: Sources and Their File Formatsmentioning

confidence: 99%

“…Because relationship terms that are highly specific are often mapped to more generalized terminology, the original source's phrasing is preserved in the relation property 2 . In addition to mapping upstream source relations to Biolink predicates, the RTX-KG2 build process coalesces edges that have the same end nodes and the same predicate (it Figure 3: Node concept types in RTX-KG2 are based on the Biolink metamodel [17,18].…”

Section: Rtx-kg2 Schema and Rtx-kg2pre Biolink Compliancementioning

confidence: 99%

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RTX-KG2: a system for building a semantically standardized knowledge graph for translational biomedicine

Wood

Glen

Kvarfordt

et al. 2021

Preprint

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Background: Biomedical translational science is increasingly leveraging computational reasoning on large repositories of structured knowledge (such as the Unified Medical Language System (UMLS), the Semantic Medline Database (SemMedDB), ChEMBL, DrugBank, and the Small Molecule Pathway Database (SMPDB)) and data in order to facilitate discovery of new therapeutic targets and modalities. Since 2016, the NCATS Biomedical Data Translator project has been working to federate autonomous reasoning agents and knowledge providers within a distributed system for answering translational questions. Within that project and within the field more broadly, there is an urgent need for an open-source framework that can efficiently and reproducibly build an integrated, standards-compliant, and comprehensive biomedical knowledge graph that can be either downloaded in standard serialized form or queried via a public application programming interface (API) that accords with the FAIR data principles. Results: To create a knowledge provider system within the Translator project, we have developed RTX-KG2, an open-source software system for building—and hosting a web API for querying—a biomedical knowledge graph that uses an Extract-Transform-Load (ETL) approach to integrate 70 knowledge sources (including the aforementioned sources) into a single knowledge graph. The semantic layer and schema for RTX-KG2 follow the standard Biolink metamodel to maximize interoperability within Translator. RTX-KG2 is currently being used by multiple Translator reasoning agents, both in its downloadable form and via its SmartAPI-registered web interface. JavaScript Object Notation (JSON) serializations of RTX-KG2 are available for download of RTX-KG2 in both the pre-canonicalized form and in canonicalized form (in which synonym concepts are merged). The current canonicalized version (KG2.7.3) of RTX-KG2 contains 6.4M concept nodes and 39.3M relationship edges with a rich set of 77 relationship types. Conclusion: RTX-KG2 is the first open-source knowledge graph of which we are aware that integrates UMLS, SemMedDB, ChEMBL, DrugBank, SMPDB, and 65 additional knowledge sources within a knowledge graph that conforms to the Biolink standard for its semantic layer and schema at the intersections of these databases. RTX-KG2 is publicly available for querying via its (API) at arax.ncats.io/api/rtxkg2/v1.2/openapi.json. The code to build RTX-KG2 is publicly available at github:RTXteam/RTX-KG2.

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Section: Sources and Their File Formatsmentioning

confidence: 99%

Section: Rtx-kg2 Schema and Rtx-kg2pre Biolink Compliancementioning

confidence: 99%

RTX-KG2: a system for building a semantically standardized knowledge graph for translational biomedicine

Wood

Glen

Kvarfordt

et al. 2021

Preprint

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“…Typically, we use the /nlp/annotate endpoint exposed by the Monarch Initiative's (Mungall et al, 2016) Biolink API. The underlying Biolink model provides a high-level data model for representing biomedical knowledge (Reese et al, 2021) and can be used to integrate across domain-specific ontologies.…”

Section: Data Annotationmentioning

confidence: 99%

Dug: A Semantic Search Engine Leveraging Peer-Reviewed Knowledge to Span Biomedical Data Repositories

Waldrop

Cheadle

Bradford

et al. 2021

Preprint

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MotivationAs the number of public data resources continues to proliferate, identifying relevant datasets across heterogenous repositories is becoming critical to answering scientific questions. To help researchers navigate this data landscape, we developed Dug: a semantic search tool for biomedical datasets that utilizes evidence-based relationships from curated knowledge graphs to find relevant datasets and explain why those results are returned.ResultsDeveloped through the National Heart, Lung, and Blood Institute’s (NHLBI) BioData Catalyst ecosystem, Dug can index more than 15,911 study variables from public datasets in just over 39 minutes. On a manually curated search dataset, Dug’s mean recall (total relevant results/total results) of 0.79 outperformed default Elasticsearch’s mean recall of 0.76. When using synonyms or related concepts as search queries, Dug’s (0.28) far outperforms Elasticsearch (0.1) in terms of mean recall.Availability and ImplementationDug is freely available at https://github.com/helxplatform/dug. An example Dug deployment is also available for use at https://helx.renci.org/ui.Contactawaldrop@rti.org or scox@renci.org

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“…Finally, we Wang et al [6] Comprehensive knowledge discovery framework to extract finegrained knowledge elements from scientific literature, and a case study on Drug Repurposing Report Generation has been presented Esteva et al [7] Retriever-ranker semantic search engine to resolve complex queries to find scientific answers Fernandez et al [8] Cause-and-effect network generated from scientific literature and presented a knowledge graph. The paper analyzes comorbidities, symptoms, and discovered over 300 candidate drugs for COVID-19 Chen et al [9] Formalization and extraction of meaningful insights from the PubMed dataset and generation of knowledge graph using two methods: co-occurrence frequency and cosine similarity Reese et al [10] Present flexible framework to integrate biomedical data for producing knowledge graphs (KGs) [1] Most of the works deal with knowledge mining related to pharmaceutical data, drug discovery or extracting information from scientific literature.…”

Section: Introductionmentioning

confidence: 99%

STOPPAGE: Spatio-temporal Data Driven Cloud-Fog-Edge Computing Framework for Pandemic Monitoring and Management

Ghosh¹,

Mukherjee²,

Ghosh³

et al. 2021

Preprint

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Several researches and evidence show the increasing likelihood of pandemics (large-scale outbreaks of infectious disease) which has far reaching sequels in all aspects of human lives ranging from rapid mortality rates to economic and social disruption across the world. In the recent time, COVID-19 (Coronavirus Disease 2019) pandemic disrupted normal human lives, and motivated by the urgent need of combating COVID-19, researchers have put significant efforts in modelling and analysing the disease spread patterns for effective preventive measures (in addition to developing pharmaceutical solutions, like vaccine). In this regards, it is absolutely necessary to develop an analytics framework by extracting and incorporating the knowledge of heterogeneous datasources to deliver insights in improving administrative policy and enhance the preparedness to combat the pandemic. Specifically, human mobility, travel history and other transport statistics have significant impacts on the spread of any infectious disease. In this direction, this paper proposes a spatio-temporal knowledge mining framework, named STOPPAGE to model the impact of human mobility and other contextual information over large geographic area in different temporal scales. The framework has two major modules: (i) Spatio-temporal data and computing infrastructure using fog/edge based architecture; and (ii) Spatio-temporal data analytics module to efficiently extract knowledge from heterogeneous data sources. Typically, we develop a Pandemic-knowledge graph to discover correlations among mobility information and disease spread, a deep learning architecture to predict the next hot-spot zones; and provide necessary support in home-health monitoring utilizing Femtolet and fog/edge based solutions. The experimental evaluations on real-life datasets related to COVID-19 in India illustrate the efficacy of the proposed methods.

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KG-COVID-19: A Framework to Produce Customized Knowledge Graphs for COVID-19 Response

Cited by 11 publications

References 8 publications

RTX-KG2: a system for building a semantically standardized knowledge graph for translational biomedicine

RTX-KG2: a system for building a semantically standardized knowledge graph for translational biomedicine

Dug: A Semantic Search Engine Leveraging Peer-Reviewed Knowledge to Span Biomedical Data Repositories

STOPPAGE: Spatio-temporal Data Driven Cloud-Fog-Edge Computing Framework for Pandemic Monitoring and Management

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