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  4. A natural language processing system for the efficient updating of highly curated pathophysiology mechanism knowledge graphs
 
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December 15, 2023
Journal Article
Title

A natural language processing system for the efficient updating of highly curated pathophysiology mechanism knowledge graphs

Abstract
Background:
Biomedical knowledge graphs (KG) have become crucial for describing biological findings in a structured manner. To keep up with the constantly changing flow of knowledge, their embedded information must be regularly updated with the latest findings. Natural language processing (NLP) has created new possibilities for automating this upkeep by facilitating information extraction from free text. However, due to annotated and labeled biomedical data limitations, the development of completely autonomous information extraction systems remains a substantial scientific and technological hurdle. This study aims to explore methodologies best suited to support the automatic extraction of causal relationships from biomedical literature with the aim of regular and rapid updating of disease-specific pathophysiology mechanism KGs.
Methods:
Our proposed approach first searches and retrieves PubMed abstracts using the desired terms and keywords. The extension corpora are then passed through the NLP pipeline for automatic information extraction. We then identify triples representing cause-and-effect relationships and encode this content using the Biological Expression Language (BEL). Finally, domain experts perform an analysis of the completeness, relevance, accuracy, and novelty of the extracted triples.
Results:
In our test scenario, which is focused on the KG regarding the phosphorylation of the Tau protein, our pipeline successfully contributed novel data, which was then subsequently used to update the KG leading to the identification of six additional upstream regulators of Tau phosphorylation.
Conclusion:
Here, it is demonstrated that the NLP-based workflow we created is capable of rapidly updating pathophysiology mechanism graphs. As a result, production-scale, semi-automated updating of pre-existing, curated mechanism graphs is enabled.
Author(s)
Babaiha, Negin Sadat
Fraunhofer-Institut für Algorithmen und Wissenschaftliches Rechnen SCAI  
Elsayed, Hassan
Fraunhofer-Institut für Algorithmen und Wissenschaftliches Rechnen SCAI  
Zhang, Bide
Fraunhofer-Institut für Algorithmen und Wissenschaftliches Rechnen SCAI  
Kaladharan, Abish
Causality Biomodels, Kinfra Hi-Tech Park, Kalamassery
Sethumadhavan, Priya
Causality Biomodels, Kinfra Hi-Tech Park, Kalamassery
Schultz, Bruce  
Fraunhofer-Institut für Algorithmen und Wissenschaftliches Rechnen SCAI  
Klein, Jürgen
Fraunhofer-Institut für Algorithmen und Wissenschaftliches Rechnen SCAI  
Freudensprung, Bruno
Kairntech SAS
Lage-Rupprecht, Vanessa
Fraunhofer-Institut für Algorithmen und Wissenschaftliches Rechnen SCAI  
Tom Kodamullil, Alpha
Fraunhofer-Institut für Algorithmen und Wissenschaftliches Rechnen SCAI  
Jacobs, Marc  
Fraunhofer-Institut für Algorithmen und Wissenschaftliches Rechnen SCAI  
Geissler, Stefan
Kairntech SAS
Madan, Sumit  
Fraunhofer-Institut für Algorithmen und Wissenschaftliches Rechnen SCAI  
Hofmann-Apitius, Martin  
Fraunhofer-Institut für Algorithmen und Wissenschaftliches Rechnen SCAI  
Journal
Artificial Intelligence in the Life Sciences  
Open Access
DOI
10.1016/j.ailsci.2023.100078
Language
English
Fraunhofer-Institut für Algorithmen und Wissenschaftliches Rechnen SCAI  
Keyword(s)
  • Knowledge graphs

  • Relation Extraction

  • Natural language processing

  • Biomedical text mining

  • Biological expression language (BEL)

  • Human brain pharmacome (HBP)

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