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Inteligencia Artificial (IA) en el desarrollo de bioterapéuticos dirigidos por el microbioma

Debarshi Roy1, Soumita Banerjee2, Alisha Ansari1

  • 1Department of Computational Biology, Indraprastha Institute of Information Technology, Delhi, India.

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Resumen

La inteligencia artificial (IA) puede analizar datos complejos del microbioma humano para identificar firmas microbianas para diagnósticos personalizados y productos bioterapéuticos vivos. Este enfoque tiene como objetivo restaurar el equilibrio microbiano intestinal y tratar enfermedades relacionadas con la disbiosis.

Palabras clave:
Inteligencia ArtificialBioterapéuticosDisbiosisMicrobioma HumanoProductos Bioterapéuticos Vivos (LBPs)Aprendizaje AutomáticoMulti-ómicasMedicina Personalizada

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Área de la Ciencia:

  • Investigación del microbioma
  • Biología computacional
  • Ciencia médica

Sus antecedentes:

  • El microbioma intestinal humano es vital para la salud, pero los desequilibrios (disbiosis) se asocian con enfermedades.
  • Los bioterapéuticos vivos (LBP) ofrecen una vía prometedora para restaurar el equilibrio microbiano.
  • La variabilidad individual del microbioma plantea desafíos para el descubrimiento de medicamentos microbianos eficaces.

Objetivo del estudio:

  • Explorar cómo la inteligencia artificial (IA) puede identificar patrones en los datos del microbioma.
  • Discutir el papel de la IA en el diagnóstico y la terapéutica derivados del microbioma.
  • Destacar el potencial de la IA en el descubrimiento de nuevas bacterias para LBP.

Principales métodos:

  • Utilización de herramientas avanzadas de IA como el aprendizaje automático (ML) y el aprendizaje profundo (DL).
  • Análisis de grandes cantidades de datos ómicos del microbioma y del huésped.
  • Identificación de "firmas" microbianas e interacciones complejas huésped-microbio.

Principales resultados:

  • La IA puede facilitar el análisis complejo requerido para la investigación del microbioma.
  • Los modelos de IA pueden identificar patrones que indican estados de enfermedad o salud.
  • La IA ayuda a descubrir candidatos potenciales para LBP al comprender las funciones microbianas.

Conclusiones:

  • La IA ofrece avances significativos en el análisis de datos personalizados del microbioma.
  • La IA puede acelerar el desarrollo de diagnósticos y terapéuticos derivados del microbioma.
  • Superar los desafíos de los datos es crucial para aprovechar todo el potencial de la IA en la medicina del microbioma.