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Aprendizaje automático en investigación e ingeniería de microbiomas

Ryan De Sotto1,2,3,4, Nikhil Aggarwal1,2,3,4, Elizabeth Huiwen Tham1,2,5,6

  • 1NUS Synthetic Biology for Clinical and Technological Innovation (SynCTI), National University of Singapore, Singapore 117456, Singapore.

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|December 22, 2025
PubMed
Resumen
Este resumen es generado por máquina.

Los avances en el aprendizaje automático (AA) en la ingeniería de microbiomas analizan comunidades microbianas complejas y predicen interacciones. Esto permite la manipulación dirigida para la atención médica y la agricultura, superando las limitaciones de datos para futuras aplicaciones.

Palabras clave:
aprendizaje automáticoingeniería de microbiomasinvestigación de microbiomasbiología sintética

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Área de la Ciencia:

  • Investigación de microbiomas
  • Biología computacional
  • Biología sintética

Sus antecedentes:

  • Los microbiomas son cruciales para la atención médica, la agricultura y la bioproducción.
  • La ingeniería de microbiomas requiere la comprensión de interacciones microbianas complejas.
  • El aprendizaje automático (AA) ofrece herramientas potentes para analizar datos de microbiomas.

Objetivo del estudio:

  • Examinar las aplicaciones transformadoras del AA en la ingeniería de microbiomas.
  • Destacar el papel del AA en el análisis de datos de microbiomas y la manipulación dirigida.
  • Discutir los desafíos y las perspectivas futuras en la ingeniería de microbiomas impulsada por el AA.

Principales métodos:

  • Utilización de algoritmos de AA para analizar grandes conjuntos de datos de microbiomas.
  • Predicción de interacciones microbianas y comportamiento de la comunidad.
  • Empleo de estrategias como la manipulación de moléculas de detección de quórum, péptidos antimicrobianos y bacteriófagos para la manipulación dirigida.

Principales resultados:

  • El AA ha tenido éxito en la identificación de comunidades microbianas y la predicción de interacciones.
  • El AA ayuda a optimizar el diseño de comunidades microbianas para funciones específicas.
  • La integración del AA con enfoques experimentales está avanzando en la ingeniería de microbiomas.

Conclusiones:

  • El AA está revolucionando la ingeniería de microbiomas, permitiendo aplicaciones novedosas.
  • Los desafíos incluyen limitaciones de datos, complejidad biológica e integración de métodos computacionales/experimentales.
  • Los avances futuros en la generación de datos, algoritmos y colaboración desbloquearán el potencial del microbioma.