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Multicellular organisms contain a variety of structurally and functionally distinct cell types, but the DNA in all the cells originated from the same parent cells. The differences in the cells can be attributed to the differential gene expression. Liver cells, whose functions include detoxification of blood, production of bile to metabolize fats, and synthesis of proteins essential for metabolism, must express a specific set of genes to perform their functions. Gene expression also varies with...
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Un nuevo marco de optimización multiobjetivo utilizando NSGA-II para la inferencia de redes de coexpresión génica

Behnam Aghajan1, Mohammad Reza Ghaemi1, Ali M Mosammam2

  • 1Department of Mathematics. Faculty of Sciences, University of Zanjan, Zanjan, Iran.

Computational biology and chemistry
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Resumen
Este resumen es generado por máquina.

Desarrollamos un novedoso método de optimización multiobjetivo utilizando el Algoritmo Genético II de Clasificación No Dominada (NSGA-II) para mejorar las redes de coexpresión génica (GCN). Este enfoque mejora la confiabilidad y la relevancia biológica de la red para el análisis de datos transcriptómicos.

Palabras clave:
ARACNERedes de coexpresión génicaOptimización multiobjetivoNSGA-IIWGCNA

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Área de la Ciencia:

  • Bioinformática
  • Biología Computacional
  • Biología de Sistemas

Sus antecedentes:

  • Las redes de coexpresión génica (GCN) son cruciales para comprender la función génica y las vías a partir de datos transcriptómicos.
  • Los datos biológicos ruidosos a menudo conducen a GCN poco confiables con conexiones espurias y estructuras poco realistas.
  • Los métodos existentes luchan por equilibrar eficazmente las propiedades de la red, como la esparsidad y la modularidad.

Objetivo del estudio:

  • Introducir un novedoso marco de optimización multiobjetivo para refinar la selección de bordes en GCN.
  • Mejorar la confiabilidad y la plausibilidad biológica de las GCN derivadas de datos transcriptómicos.
  • Optimizar simultáneamente múltiples características de la red, incluida la esparsidad, la modularidad y la topología libre de escala.

Principales métodos:

  • Se utilizó la Transformación de Estabilización de Varianza (VST) para la normalización de datos de RNA-seq.
  • Se empleó la correlación de rangos de Spearman para una estimación robusta de la coexpresión.
  • Se integró el Algoritmo Genético II de Clasificación No Dominada (NSGA-II) para la optimización de redes multiobjetivo.
  • Se incorporaron pruebas de permutación y remuestreo de bootstrap para la evaluación de la significancia y la estabilidad.

Principales resultados:

  • El enfoque propuesto basado en NSGA-II generó GCN más dispersas y modulares en comparación con WGCNA y ARACNE.
  • Las redes optimizadas exhibieron una mejor adherencia a las propiedades de redes libres de escala en conjuntos de datos de microarrays y RNA-seq.
  • El método demostró un rendimiento robusto en conjuntos de datos transcriptómicos heterogéneos (GSE10245 y GSE102349).

Conclusiones:

  • La estrategia impulsada por la optimización proporciona un método robusto para construir GCN de alta calidad.
  • Este enfoque ofrece un avance significativo para estudios genómicos integradores y el descubrimiento de biomarcadores.
  • El marco tiene el potencial de mejorar la modelización de mecanismos de enfermedades complejas.