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Updated: Aug 22, 2025

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Published on: October 15, 2019
Towards a knowledge graph for pre-/probiotics and microbiota-gut-brain axis diseases
Ting Liu1,2, Gongjin Lan3, K Anton Feenstra1
1Department of Computer Science, Center for Integrative Bioinformatics, Vrije Universiteit Amsterdam, 1081 HV, Amsterdam, The Netherlands.
This study introduces a novel knowledge graph to integrate biological data from scientific literature and databases. This approach enhances the discovery of relationships between pre-/probiotics and microbiota-gut-brain axis diseases.
Area of Science:
- Bioinformatics
- Computational Biology
- Microbiome Research
Background:
- Scientific literature contains rich biological relationship data but lacks machine-readable structure.
- Existing biological databases are structured for analysis but lack comprehensive knowledge.
- Integrating these resources is crucial for advanced biological querying and discovery.
Purpose of the Study:
- To develop a method for constructing comprehensive knowledge graphs from scientific publications and databases.
- To investigate the relationships between pre-/probiotics and microbiota-gut-brain axis diseases using the constructed knowledge graph.
- To demonstrate the enhanced querying capabilities and biological insights gained from integrated knowledge bases.
Main Methods:
- Creation of two knowledge bases: ppstatement (manual annotations) and ppconcept (automatic annotations).
- Integration of these knowledge bases with public databases: MeSH, UMLS, and SNOMED CT to form the Pre-/Probiotics Knowledge Graph (PPKG).
- Validation using four biological query cases to assess performance and the added value of integration.
Main Results:
- The PPKG successfully integrates manually curated and automatically annotated data with public ontologies.
- Query cases demonstrated the retrieval of co-occurring concepts relevant to pre-/probiotics and microbiota-gut-brain axis diseases.
- Combining knowledge bases within PPKG yielded more comprehensive results than using them individually.
Conclusions:
- The developed approach enables the construction of comprehensive knowledge graphs from diverse biological data sources.
- The Pre-/Probiotics Knowledge Graph (PPKG) facilitates novel research queries, such as identifying beneficial pre-/probiotic combinations for specific diseases.
- This integrated knowledge graph has the potential to uncover new biological insights and guide therapeutic strategies.
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