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Hipótesis bayesianas de mezcla de expertos de grafos hiperesféricos descifran la interacción célula-célula en

Wenchuan Zhang, Yujian Lee, Ricky Yuen-Tan Hou

    IEEE transactions on computational biology and bioinformatics
    |February 2, 2026
    PubMed
    Resumen
    Este resumen es generado por máquina.

    B-HGME, un nuevo marco computacional, mapea con precisión las interacciones célula-célula y los dominios espaciales en tejidos utilizando datos de transcriptómica espacial. Supera las limitaciones de los métodos existentes, lo que permite descubrimientos biológicos novedosos y la generación de hipótesis.

    Palabras clave:
    transcriptómica espacialinteracción célula-célulaaprendizaje automáticomodelado de grafosbiología de sistemascomputacionalbiologíagenómicaanálisis de datosdescubrimiento de fármacos

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    Área de la Ciencia:

    • Biología Computacional
    • Biología de Sistemas
    • Genómica

    Sus antecedentes:

    • La transcriptómica espacial (ST) avanza en la biología de tejidos al proporcionar la expresión génica con contexto espacial.
    • Los métodos computacionales existentes para las interacciones célula-célula (CCI) luchan con la proximidad fija, las bases de datos limitadas y el sobre-suavizado.
    • Las limitaciones dificultan el descubrimiento de CCI complejas, direccionales y de largo alcance, cruciales para comprender la organización tisular y la enfermedad.

    Objetivo del estudio:

    • Introducir B-HGME (Bayesian Hyperspherical Graph Mixture of Experts), un marco escalable y no supervisado para el análisis de datos de ST.
    • Delinear conjuntamente dominios espaciales e inferir redes de CCI con cuantificación de la incertidumbre.
    • Superar las limitaciones de los métodos existentes para descubrir CCI heterogéneos, direccionales y de largo alcance.

    Principales métodos:

    • Integrar grafos espaciales y de regulación génica en una estructura de doble escala.
    • Codificar representaciones celulares en una hiperesfera unitaria utilizando paso de mensajes acoplado.
    • Emplear una mezcla bayesiana de expertos con una red de puerta regularizada por Dirichlet para la decodificación de bordes.

    Principales resultados:

    • Logra una precisión de agrupación espacial de última generación en diversos conjuntos de datos de ST.
    • Descubre CCI biológicamente coherentes y diversos, incluidas interacciones novedosas más allá de los pares de ligandos-receptores curados.
    • Demuestra la localización precisa de marcadores canónicos, confirmando la fidelidad bioquímica a la resolución de un solo gen.

    Conclusiones:

    • B-HGME proporciona una herramienta poderosa para la biología de sistemas espaciales y la generación de hipótesis.
    • El marco permite el descubrimiento de circuitos novedosos de ligandos-receptores, ofreciendo información mecanicista sobre el desarrollo y la enfermedad.
    • B-HGME facilita la inferencia de CCI interpretable y confiable con estimaciones de incertidumbre basadas en principios.