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  4. Unraveling the co-morbidity between COVID-19 and neurodegenerative diseases through multi-scale graph analysis: A systematic investigation of biological databases and text mining
 
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December 2025
Journal Article
Title

Unraveling the co-morbidity between COVID-19 and neurodegenerative diseases through multi-scale graph analysis: A systematic investigation of biological databases and text mining

Abstract
The COVID-19 pandemic has generated a vast volume of research, yet much of it focuses on individual diseases, overlooking complex comorbidity relationships. While extensive literature exists on both neurodegenerative diseases (NDDs), such as Alzheimer's and Parkinson's, and COVID-19, their intersection remains underexplored. Co-morbidity modeling is crucial, particularly for hospitalized patients often presenting with multiple conditions. This study investigates the interplay between COVID-19 and NDDs by integrating knowledge graphs (KGs) built from curated biomedical datasets and text mining tools. We performed comprehensive analyses-including path analysis, phenotype coverage, and mapping of cellular and genetic factors-across multiple KGs, such as Pri-meKG, DrugBank, OpenTargets, and those generated via natural language processing (NLP) methods. Our findings reveal notable variability in graph density and connectivity, with each KG offering unique insights into molecular and phenotypic links between COVID-19 and NDDs. Key genetic and inflammatory markers, especially immune response genes, consistently appeared across graphs, suggesting a shared pathogenic basis. By unifying structured biological data with unstructured textual evidence, we enhance co-morbidity modeling and improve recall in identifying mechanisms underlying COVID-19-NDD interactions. This integrative framework supports the development of a co-morbidity hypothesis database aimed at facilitating therapeutic target discovery. All data, methods, and instructions for accessing the co-morbidity hypothesis database are publicly available at: http s://github.com/SCAI-BIO/covid-NDD-comorbidity-NLP.
Author(s)
Babaiha, Negin
Fraunhofer-Institut für Algorithmen und Wissenschaftliches Rechnen SCAI  
Geissler, Stefan
Nibart, Vincent
Fraunhofer-Institut für Algorithmen und Wissenschaftliches Rechnen SCAI  
Atas Güvenilir, Heval
Fraunhofer-Institut für Algorithmen und Wissenschaftliches Rechnen SCAI  
Bharadhwaj, Vinay Srinivas  
Fraunhofer-Institut für Algorithmen und Wissenschaftliches Rechnen SCAI  
Tom Kodamullil, Alpha
Fraunhofer-Institut für Algorithmen und Wissenschaftliches Rechnen SCAI  
Klein, Jürgen
Fraunhofer-Institut für Algorithmen und Wissenschaftliches Rechnen SCAI  
Jacobs, Marc  
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
File(s)
Download (6.83 MB)
Rights
CC BY 4.0: Creative Commons Attribution
DOI
10.1016/j.ailsci.2025.100138
10.24406/publica-5388
Additional full text version
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English
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