Graph neural networks-based prediction of drug gene association of P2X receptors in periodontal pain

In this study, 921 drug-gene interactions involving P2X receptors were accessed through https://www.probes-drugs.org/. These interactions underwent meticulous annotation, preprocessing, and subsequent utilization to train and assess GNNs. Furthermore, leveraging Cytoscape, the CytoHubba plugin, and other bioinformatics tools, gene expression networks were constructed to pinpoint hub genes within these interactions. Through analysis, SLC6A3, SLC6A2, FGF1, GRK2, and PLA2G2A were identified as central hub genes within the context of P2X receptor-mediated drug-gene interactions. Despite achieving a 65 percent accuracy rate, the GNN model demonstrated suboptimal predictive power for gene-drug interactions associated with oral pain. Hence, further refinements and enhancements are imperative to unlock its full potential in elucidating and targeting pathways underlying oral pain mechanisms.PMID:38680473 | PMC:PMC11053325 | DOI:10.1016/j.jobcr.2024.04.008

Source: Genomics Proteomics ... - April 29, 2024 Category: Genetics & Stem Cells Authors: Pradeep Kumar Yadalam Prabhu Manickam Natarajan Seyed Ali Mosaddad Artak Heboyan Source Type: research