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

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Published on: October 13, 2023

BioGraph: Data Model for Linking and Querying Diverse Biological Metadata.

Aleksandar N Veljković¹, Yuriy L Orlov^2,3,4, Nenad S Mitić¹

¹Faculty of Mathematics, University of Belgrade, Studentski trg 16, 11158 Belgrade, Serbia.

International Journal of Molecular Sciences

|April 28, 2023

Summary

BioGraph is a novel model for integrating diverse biological data, enabling complex pattern discovery. It facilitates joint data retrieval from multiple sources, overcoming compatibility challenges for gene function and disease research.

Keywords:

BioGraph associations with the diseases connecting biological data gene network metadata query data properties

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Published on: February 8, 2017

Area of Science:

Bioinformatics
Computational Biology
Systems Biology

Background:

Data integration is crucial for studying gene function, diseases, and regulatory networks.
Heterogeneous data schemas and access methods across databases complicate joint data retrieval.
Biological entities can be linked through shared properties across disparate datasets.

Purpose of the Study:

To propose BioGraph, a model for connecting and retrieving information from linked biological data from diverse sources.
To address the challenges of data compatibility and joint data fetching for biological research.

Main Methods:

Developed BioGraph, a novel data model for integrating information from multiple biological datasets.
Tested the model on metadata from five diverse public datasets.
Constructed a knowledge graph comprising over 17 million model objects.

Main Results:

Successfully built a knowledge graph with 2.5 million individual biological entity objects.
Demonstrated the model's capability to connect and retrieve information from linked biological data.
Enabled the selection of complex patterns discoverable only through multi-source data joining.

Conclusions:

BioGraph effectively integrates heterogeneous biological data, enabling comprehensive analysis.
The model facilitates the discovery of complex biological insights by linking diverse data sources.
BioGraph supports advanced querying and retrieval for gene function, disease association, and network reconstruction.