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Evidence-based Mechanistic Integrative Modeling of Stroke Risk Factors: A Translational Research Study

Mahdi Habibi Koolaee, Leila Shahmoradi, Sharareh R. Niakan Kalhori, Hossein Ghannadan, Erfan Younesi



Introduction: According to global statistics, stroke is known as the main health problem in the world. Many clinical and molecular research, which are stored in the different repository with the various format have been conducted in the area of stroke domain. The heterogeneity of these research data does not make a comprehensive view of the disease. Recently, translational research has been developed to fill the gap between these studies. In this study, we used the integrative disease modeling method to model the underlying mechanism of stroke risk factors.

Material and Methods: This study was conducted in three steps: data gathering, model construction, and mechanism discovery. First, using semantic and information retrieval tools, we extracted the cause and effect statement from the literature to create the mechanistic model, and the validated molecular data to evaluate the constructed model. Then, the integrative model was created and evaluated. Finally, we used Gene Set Enrichment Analysis to identify the main biological process and signaling pathways in the mechanism of the disease. 

Results: In the evidence-based information retrieval from the literature, 1837 causal statement was extracted. The initial network was created with 648 nodes (molecular, clinical, and environmental factors) and 1837 edges (interactions). Also, 51 genes/proteins and nine single nucleotide polymorphisms were matched with data in the model. The inflammatory response, response to lipid, regulation of body fluid levels, and regulation of response to stress, complement and coagulation cascades, and PPAR signaling pathway were the main biological processes and signaling pathways enriched in GSEA analysis.

Conclusion:  This study showed that we can identify the underlying mechanism of stroke risk factors and use a proper strategy to prevent it, using Integrative Disease Modeling.


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DOI: http://dx.doi.org/10.30699/fhi.v10i1.263


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