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Métodos computacionales para la predicción de péptidos señal: de modelos estadísticos a aprendizaje profundo

Qianmao Wen1, Xinyu Li1, Jiaxing Song1

  • 1School of Computer Science and Technology, Hainan University, Haikou 570228, China.

Biotechnology advances
|February 2, 2026
PubMed
Resumen

Los métodos computacionales para identificar péptidos señal han evolucionado significativamente, pasando de algoritmos básicos a aprendizaje profundo para mejorar la precisión en la predicción de la localización y el transporte de proteínas.

Palabras clave:
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Área de la Ciencia:

  • Biología Molecular
  • Bioinformática

Sus antecedentes:

  • Los péptidos señal son secuencias de aminoácidos N-terminales cruciales para la localización y el transporte de proteínas.
  • Los métodos de identificación experimental son laboriosos y costosos, lo que requiere enfoques computacionales.

Objetivo del estudio:

  • Revisar y resumir sistemáticamente los métodos computacionales para la predicción de péptidos señal.
  • Analizar la evolución de estos métodos y sus diseños de marco.
  • Identificar limitaciones y discutir oportunidades futuras en la identificación computacional de péptidos señal.

Principales métodos:

  • Revisión de enfoques computacionales desarrollados durante las últimas dos décadas.
  • Comparación de la precisión de la predicción y los marcos metodológicos.
  • Análisis de limitaciones y tendencias emergentes en el campo.

Principales resultados:

  • Los métodos computacionales han progresado desde algoritmos estadísticos y basados en reglas hasta técnicas avanzadas de aprendizaje profundo.
  • Se ha observado una mejora continua en la precisión de la predicción.
  • Se han propuesto varios marcos computacionales, cada uno con diseños y resultados distintos.

Conclusiones:

  • Las herramientas computacionales son esenciales para la identificación eficiente de péptidos señal.
  • El desarrollo futuro debe centrarse en la evaluación unificada, la interpretación biológica y la modelización generativa.
  • Los avances tienen como objetivo mejorar la precisión y la interpretabilidad de los marcos de predicción de péptidos señal.