CovidGraph: a graph to fight COVID-19

Gütebier, Lea; Bleimehl, Tim; Henkel, Ron; Munro, J.; Müller, Sebastian Alexander; Morgner, Axel; Laenge, Jakob; Pachauer, Anke; Erdl, Alexander; Weimar, Jens; Langendorf, Kirsten Walther; Vialard, Vincent; Liebig, Thorsten; Preuße, M; Waltemath, Dagmar; Jarasch, Alexander

doi:10.1093/bioinformatics/btac592

Cited by 16 publications

(9 citation statements)

References 14 publications

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“…CovidGraph is build on a Neo4j 2 graph database engine which can be accessed through different user interfaces [2]. One of these interfaces is SemSpect, a visualisation tool for interactive exploration of knowledge graphs [4].…”

Section: Methods and Resultsmentioning

confidence: 99%

“…HealthECCO is a community of graph enthusiasts who first united forces in the wake of the COVID-19 pandemic. CovidGraph, a tool for efficient data access and exploration to support research efforts relating to COVID-19 [2], is HealthECCO's fast response to the pandemic. As of today, CovidGraph integrates a variety of data sources, e.g., scientific publications, study protocols, and simulation studies.…”

Section: Introductionmentioning

confidence: 99%

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Exploring Use Cases for CovidGraph

Gütebier

Henkel

Niklas

et al. 2023

Caring Is Sharing – Exploiting the Value in Data for Health and Innovation

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HealthECCO is the driving force behind the COVID-19 knowledge graph spanning multiple biomedical data domains. One way to access CovidGraph is SemSpect, an interface designed for data exploration in graphs. To showcase the possibilities that arise from integrating a variety of COVID-19 related data sources over the last three years, we present three use cases from the (bio-)medical domain. Availability: The project is open source and freely available from: https://healthecco.org/covidgraph/. The source code and documentation are available on GitHub: https://github.com/covidgraph.

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Section: Methods and Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Exploring Use Cases for CovidGraph

Gütebier

Henkel

Niklas

et al. 2023

Caring Is Sharing – Exploiting the Value in Data for Health and Innovation

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“…The implementation details on the database (“DBMS”), available interfaces (“Interface”) and the data (“Data”) are listed. The features interactivity (“Int”), flexibility (“Flex”), collaboration (“Coll”) and export(“Exp”) are assessed qualitatively System DBMS Interface Int Flex Coll Exp Data MyPal [ 22 ] Unknown Web, Mobile App ✓ ✗ ✗ ✓ Patient data, demographics, treatment, patient-reported outcomes CovidGraph [ 23 ] Neo4j Web ✓ ✗ ✗ ✓ Publications, patents, clinical trials, genes, transcripts, proteins, pathways, statistics, systems biology data OncoKB [ 24 ] MySQL Web, CLI ✓ ✗ ✗ ✓ Cancer mutations, drugs with corresponding cancer type & targeted mutations BioGraph [ 16 ] Neo4j Web ✓ ✗ ✗ ✓ Genes, proteins, miRNA-related data, pathways, functional annotations LinkedImm [ 12 ] Neo4j Web ✓ ✗ ✗ ✓ Immunological data, genes, pathways, transcriptional profiling data Graph4Med Neo4j Web ...…”

Section: Discussionmentioning

confidence: 99%

Graph4Med: a web application and a graph database for visualizing and analyzing medical databases

et al. 2022

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Background Medical databases normally contain large amounts of data in a variety of forms. Although they grant significant insights into diagnosis and treatment, implementing data exploration into current medical databases is challenging since these are often based on a relational schema and cannot be used to easily extract information for cohort analysis and visualization. As a consequence, valuable information regarding cohort distribution or patient similarity may be missed. With the rapid advancement of biomedical technologies, new forms of data from methods such as Next Generation Sequencing (NGS) or chromosome microarray (array CGH) are constantly being generated; hence it can be expected that the amount and complexity of medical data will rise and bring relational database systems to a limit. Description We present Graph4Med, a web application that relies on a graph database obtained by transforming a relational database. Graph4Med provides a straightforward visualization and analysis of a selected patient cohort. Our use case is a database of pediatric Acute Lymphoblastic Leukemia (ALL). Along routine patients’ health records it also contains results of latest technologies such as NGS data. We developed a suitable graph data schema to convert the relational data into a graph data structure and store it in Neo4j. We used NeoDash to build a dashboard for querying and displaying patients’ cohort analysis. This way our tool (1) quickly displays the overview of patients’ cohort information such as distributions of gender, age, mutations (fusions), diagnosis; (2) provides mutation (fusion) based similarity search and display in a maneuverable graph; (3) generates an interactive graph of any selected patient and facilitates the identification of interesting patterns among patients. Conclusion We demonstrate the feasibility and advantages of a graph database for storing and querying medical databases. Our dashboard allows a fast and interactive analysis and visualization of complex medical data. It is especially useful for patients similarity search based on mutations (fusions), of which vast amounts of data have been generated by NGS in recent years. It can discover relationships and patterns in patients cohorts that are normally hard to grasp. Expanding Graph4Med to more medical databases will bring novel insights into diagnostic and research.

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“…The knowledge graph approach is becoming one of the most critical tools for drug discovery and drug repurposing. For example, a knowledge graph has been built for COVID-19 research [ 181 ]. This graph has captured concurrent information about drugs, targets and COVID-19.…”

Section: Computational Methodology For Natural-product-based Drug Dis...mentioning

confidence: 99%

Lead/Drug Discovery from Natural Resources

Eichler

Klausner

et al. 2022

Molecules

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Natural products and their derivatives have been shown to be effective drug candidates against various diseases for many years. Over a long period of time, nature has produced an abundant and prosperous source pool for novel therapeutic agents with distinctive structures. Major natural-product-based drugs approved for clinical use include anti-infectives and anticancer agents. This paper will review some natural-product-related potent anticancer, anti-HIV, antibacterial and antimalarial drugs or lead compounds mainly discovered from 2016 to 2022. Structurally typical marine bioactive products are also included. Molecular modeling, machine learning, bioinformatics and other computer-assisted techniques that are very important in narrowing down bioactive core structural scaffolds and helping to design new structures to fight against key disease-associated molecular targets based on available natural products are considered and briefly reviewed.

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CovidGraph: a graph to fight COVID-19

Cited by 16 publications

References 14 publications

Exploring Use Cases for CovidGraph

Exploring Use Cases for CovidGraph

Graph4Med: a web application and a graph database for visualizing and analyzing medical databases

Lead/Drug Discovery from Natural Resources

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