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Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
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Kinetics describes the rate and path by which a reaction occurs. In contrast, thermodynamics deals with state functions and describes the properties, behavior, and components of a system. It is not concerned with the path taken by the process and cannot address the rate at which a reaction occurs. Although it does provide information about what can happen during a reaction process, it does not describe the detailed steps of what appears on an atomic or a molecular level. On the other hand,...
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scOTM: Un marco de aprendizaje profundo para predecir respuestas de perturbación de una sola célula con grandes

Yuchen Wang1, Tianchi Lu1, Xingjian Chen2

  • 1Department of Computer Science, City University of Hong Kong, Kowloon Tong, Hong Kong SAR 999077, China.

Bioengineering (Basel, Switzerland)
|August 28, 2025
PubMed
Resumen

Desarrollamos scOTM, un modelo de aprendizaje profundo que predice respuestas de fármacos de una sola célula a partir de datos no emparejados. Este método supera las limitaciones de los enfoques existentes mediante el modelado flexible de los cambios de transcripción y la generalización a nuevos tipos de células.

Palabras clave:
aprendizaje profundomodelo de lenguaje grandetransporte óptimoPrevisión de las perturbaciones de una sola célula

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Área de la Ciencia:

  • Biología computacional
  • La genómica
  • Aprendizaje automático

Sus antecedentes:

  • El modelado preciso de las respuestas farmacológicas de una sola célula es crucial para la atención médica.
  • Los métodos actuales luchan con datos no emparejados y carecen de interpretabilidad biológica.
  • Los modelos existentes a menudo imponen alineaciones previas restrictivas, lo que limita su expresividad.

Objetivo del estudio:

  • Desarrollar un marco de aprendizaje profundo, scOTM, para predecir respuestas de perturbación de una sola célula a partir de datos no emparejados.
  • Mejorar la generalización a tipos celulares invisibles y mejorar la interpretabilidad biológica.
  • Para superar las limitaciones de los métodos existentes en el manejo de datos no emparejados y rígidas restricciones previas.

Principales métodos:

  • scOTM integra el conocimiento biológico de los grandes modelos de lenguaje en un autoencoder variacional.
  • La regularización de la discrepancia media máxima permite el modelado flexible de los cambios de transcripción.
  • El transporte óptimo establece asignaciones interpretables entre las distribuciones de células de control y perturbadas.

Principales resultados:

  • scOTM supera a los métodos existentes para predecir las respuestas de todo el transcriptoma e identificar genes expresados diferencialmente.
  • El marco demuestra una robustez superior en escenarios con datos limitados.
  • scOTM muestra fuertes capacidades de generalización en diversos tipos de células.

Conclusiones:

  • scOTM proporciona un marco potente y flexible para predecir las respuestas farmacológicas de una sola célula.
  • El método mejora la comprensión biológica al ofrecer incrustaciones interpretables y modelado flexible.
  • scOTM avanza en el campo mediante el manejo eficaz de datos no emparejados y la generalización a nuevos tipos de células.