| Autor: |
Patidar K; Department of Chemical and Biological Engineering, University at Buffalo, Buffalo, NY 14260, USA., Deng JH; Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA 30332, USA., Mitchell CS; Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA 30332, USA.; Center for Machine Learning at Georgia Tech, Georgia Institute of Technology, Atlanta, GA 30332, USA., Ford Versypt AN; Department of Chemical and Biological Engineering, University at Buffalo, Buffalo, NY 14260, USA.; Department of Biomedical Engineering, University at Buffalo, Buffalo, NY 14260, USA.; Institute for Artificial Intelligence and Data Science, University at Buffalo, Buffalo, NY 14260, USA. |
| Jazyk: |
angličtina |
| Zdroj: |
International journal of molecular sciences [Int J Mol Sci] 2024 Apr 19; Vol. 25 (8). Date of Electronic Publication: 2024 Apr 19. |
| DOI: |
10.3390/ijms25084503 |
| Abstrakt: |
Diabetic kidney disease (DKD) is the leading cause of end-stage renal disease worldwide. This study's goal was to identify the signaling drivers and pathways that modulate glomerular endothelial dysfunction in DKD via artificial intelligence-enabled literature-based discovery. Cross-domain text mining of 33+ million PubMed articles was performed with SemNet 2.0 to identify and rank multi-scalar and multi-factorial pathophysiological concepts related to DKD. A set of identified relevant genes and proteins that regulate different pathological events associated with DKD were analyzed and ranked using normalized mean HeteSim scores. High-ranking genes and proteins intersected three domains-DKD, the immune response, and glomerular endothelial cells. The top 10% of ranked concepts were mapped to the following biological functions: angiogenesis, apoptotic processes, cell adhesion, chemotaxis, growth factor signaling, vascular permeability, the nitric oxide response, oxidative stress, the cytokine response, macrophage signaling, NFκB factor activity, the TLR pathway, glucose metabolism, the inflammatory response, the ERK/MAPK signaling response, the JAK/STAT pathway, the T-cell-mediated response, the WNT/β-catenin pathway, the renin-angiotensin system, and NADPH oxidase activity. High-ranking genes and proteins were used to generate a protein-protein interaction network. The study results prioritized interactions or molecules involved in dysregulated signaling in DKD, which can be further assessed through biochemical network models or experiments. |
| Databáze: |
MEDLINE |
| Externí odkaz: |
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