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TransSE: Un modelo predictivo basado en aprendizaje por transferencia para distinguir potenciadores y potenciadores

Yin Shen, Xuan Xu, Shuaibin Wang

IEEE transactions on computational biology and bioinformatics

|January 23, 2026

Resumen

TransSE, un modelo de aprendizaje profundo, predice con precisión los superpotenciadores (SE) utilizando el aprendizaje por transferencia entre especies. Este marco mejora la comprensión de la regulación génica y las variantes asociadas a enfermedades.

Sus antecedentes:

Los superpotenciadores (SE) son cruciales para la expresión génica y el destino celular.
Los métodos computacionales existentes para la identificación de SE carecen de precisión y generalización inter-especies.

Conclusiones:

TransSE ofrece un método robusto y preciso para la predicción de SE con capacidades inter-especies mejoradas.
El marco integra características reguladoras conservadas y específicas de la especie para un rendimiento mejorado.
TransSE proporciona una interfaz web fácil de usar para la predicción automatizada de SE, el análisis de motivos y la evaluación del impacto de variantes, lo que ayuda en los estudios de variación asociados a enfermedades.

Palabras clave:

aprendizaje por transferencia superpotenciadores identificación de potenciadores aprendizaje profundo modelado predictivo genómica bioinformática

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