Redes neuronales de grafos con conciencia del conocimiento para asociaciones fármaco-gen TRPV1 en el dolor periodontal

Autores/as

  • Pradeep K. Yadalam Department of Periodontics, Saveetha Dental College and Hospitals, Saveetha Institute of Medical and Technical Sciences (SIMATS), Chennai - 600 077.Tamilnadu, India. Autor/a https://orcid.org/0000-0002-6653-4123
  • Saravagya Sharma Department of Periodontics, Saveetha Dental College and Hospitals, Saveetha Institute of Medical and Technical Sciences (SIMATS), Chennai - 600 077.Tamilnadu, India. Autor/a https://orcid.org/0009-0004-8883-5041
  • Carlos M. Ardila Department of Periodontics, Saveetha Dental College and Hospitals, Saveetha Institute of Medical and Technical Sciences (SIMATS), Chennai - 600 077.Tamilnadu, India. Basic Sciences Department, Faculty of Dentistry, Biomedical Stomatology Research Group, Universidad de Antioquia U de A, Medellín, 050010, Colombia. Autor/a https://orcid.org/0000-0002-3663-1416

DOI:

https://doi.org/10.15517/1ehs9706

Palabras clave:

Dolor periodontal; TRPV1; Grafos; Redes neuronales; Grafos de conocimiento.

Resumen

TRPV1 (Receptor Potencial Transitorio Vanilloide 1) es una proteína crítica en la patogénesis del dolor persistente, activado por estímulos nocivos e intermediarios inflamatorios asociados con la periodontitis. Este estudio investiga las interacciones fármaco-gen que involucran a TRPV1 para dilucidar su papel en el dolor periodontal. Se emplearon Redes Neuronales de Grafos con Conciencia del Conocimiento (KGNs) para modelar y analizar las complejas relaciones entre fármacos, genes y receptores de dolor en tejidos periodontales. Al aprovechar conjuntos de datos biológicos, incluyendo el canal iónico TRPV1, interacciones de receptores de dolor y perfiles de expresión génica, el estudio tiene como objetivo identificar posibles dianas terapéuticas y estrategias para el manejo personalizado del dolor periodontal. Se integraron genes diferencialmente expresados (DEGs) con datos de fármacos y genes para modelar sistemas biológicos e informar la priorización terapéutica. Se emplearon tres modelos basados en grafos: Red Neuronal Convolucional de Grafos (GCN) como línea base, GCN Residual (ResGCN) para estabilidad y GCN basado en atención (AttGCN) para ponderación dinámica de nodos. Entre los modelos, ResGCN demostró un rendimiento superior con una precisión del 93.75% y la menor pérdida final, destacando su robustez en la predicción de asociaciones fármaco-gen. Este resultado apoya la utilidad potencial de ResGCN en el modelado preciso de los mecanismos de dolor mediados por TRPV1 y en la guía de la priorización terapéutica. La aplicación de KGNs ha proporcionado valiosos conocimientos sobre las interacciones fármaco-gen TRPV1 en el contexto del dolor periodontal. Los hallazgos enfatizan el potencial de usar ResGCN en la optimización y el tratamiento terapéutico. Sin embargo, persisten desafíos como la calidad de los datos y la complejidad biológica.

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Publicado

2025-07-21