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A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

Published on: October 13, 2023

Enabling semantic queries across federated bioinformatics databases.

Ana Claudia Sima^1,2,3,4, Tarcisio Mendes de Farias^2,3,4,5, Erich Zbinden^1,4

¹ZHAW Zurich University of Applied Sciences, Obere Kirchgasse 2, 8400 Winterthur Switzerland.

Database : the Journal of Biological Databases and Curation

|November 8, 2019

Summary

This summary is machine-generated.

This study presents an ontology-based federated approach to integrate heterogeneous biological databases, enabling new insights from diverse data sources like gene expression and protein information.

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Bioinformatics Resources for the Study of Glycan-Mediated Protein Interactions

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Published on: January 20, 2022

Area of Science:

Bioinformatics
Computational Biology
Data Science

Background:

Biological data integration is crucial for novel insights.
Data heterogeneity across sources hinders interoperability.
Existing biological databases lack seamless data sharing.

Purpose of the Study:

To develop an ontology-based federated approach for integrating heterogeneous biological data.
To enhance interoperability among disparate biological databases.
To facilitate data access for both technical and non-technical users.

Main Methods:

Defined a new semantic model (GenEx) for gene expression data.
Developed relational-to-RDF mappings for the Bgee database.
Utilized SPARQL endpoints for data access and federated querying.
Identified intersection points for joint queries across databases.

Main Results:

Successfully integrated data from Bgee, OMA, and UniProtKB.
Established public SPARQL endpoints for Bgee and OMA data.
Enabled joint queries across heterogeneous biological data stores.
Developed a natural language interface for non-technical users.

Conclusions:

The ontology-based federated approach effectively addresses biological data heterogeneity.
This integration facilitates cross-database querying and knowledge discovery.
The developed system enhances accessibility and usability of biological data.