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A Practical Guide to Phylogenetics for Nonexperts

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Identificación eficiente de los sitios de alineación filogenéticamente informativos a través del aprendizaje escaso.

Carlos G Schrago¹

¹Department of Genetics, Federal University of Rio de Janeiro, RJ, Brazil.

Molecular phylogenetics and evolution

|February 22, 2026

Resumen

Desarrollamos un nuevo método que utiliza el aprendizaje escaso para identificar sitios clave en los datos genéticos para una reconstrucción precisa del árbol evolutivo. Este enfoque identifica de manera eficiente los sitios con información filogenética, mejorando los análisis filogenómicos.

Palabras clave:

Alineación de recorte de la alineación.Indels Indels es el nombre que se le da a una industria.Lasso regresión de regresión.Selección de la selección del marcador.La información filogenética es muy informativa.Aprendizaje escaso Aprendizaje escaso.

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Área de la Ciencia:

Filogenética y Biología Evolutiva.
Biología computacional Biología computacional.
La genómica es la genómica.

Sus antecedentes:

La reconstrucción precisa del árbol filogenético se basa en la identificación de sitios filogenéticamente informativos en múltiples alineaciones de secuencias.
Los métodos actuales a menudo dependen de topologías o heurísticas predefinidas, lo que limita su aplicabilidad e interpretabilidad.

Objetivo del estudio:

Desarrollar un marco topológico-agnóstico para cuantificar la información filogenética del sitio.
Para identificar el subconjunto mínimo de sitios cruciales para la señal filogenética utilizando el aprendizaje disperso.

Principales métodos:

Aprendizaje escaso empleado a través de la regresión de Lasso (menos reducción absoluta y operador de selección).
Log-likelihoods del sitio modelado como predictores de la probabilidad del árbol a través de topologías aleatorias.
Validado utilizando conjuntos de datos de mamíferos simulados y empíricos.

Principales resultados:

Los sitios seleccionados por lasso produjeron topologías de árboles casi idénticas a las de las alineaciones completas.
Un proxy basado en la entropía efectivamente aproximó los resultados de Lasso para la eficiencia computacional.
Demostró la identificación de un subconjunto mínimo de sitios filogenéticamente informativos.

Conclusiones:

El aprendizaje escaso ofrece un método práctico, escalable y de principios para evaluar y optimizar los datos filogenéticos.
El marco desarrollado proporciona una métrica objetiva para los sitios filogenéticamente informativos.
Este enfoque mejora la eficiencia y la precisión en los análisis filogenómicos.