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Advances in genomics have profoundly influenced drug discovery by increasing both the speed and accuracy of pharmaceutical development. Pharmacogenomics, which examines how genetic variation influences drug response, facilitates the identification of novel therapeutic targets and enables patient stratification for personalized treatment. These strategies contribute to improved drug efficacy, minimized adverse effects, and more efficient clinical trial design.Mapping genetic differences...
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Genomics is the science of genomes: it is the study of all the genetic material of an organism. In humans, the genome consists of information carried in 23 pairs of chromosomes in the nucleus, as well as mitochondrial DNA. In genomics, both coding and non-coding DNA is sequenced and analyzed. Genomics allows a better understanding of all living things, their evolution, and their diversity. It has a myriad of uses: for example, to build phylogenetic trees, to improve productivity and...
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The human genome is over 99.9% identical between individuals, yet genetic differences exist at millions of bases. The human genome contains approximately 3 million variant positions per individual, many of which are heterozygous, contributing to genetic diversity and individual traits. Genetic variations include single-nucleotide polymorphisms (SNPs), insertions, deletions, and copy number variations (CNVs).SNPs, the most common variation, involve single-base changes in DNA. These can be...
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DRIVE-KG: Mejora del descubrimiento de asociaciones entre variantes y fenotipos en enfermedades complejas

Ananya Rajagopalan1, Tram Anh Nguyen1, Lindsay A Guare1

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La integración de datos multiómicos con DRIVE-KG avanza en la comprensión de la endometriosis. Este grafo de conocimiento identifica nuevas asociaciones genéticas y mejora la clasificación de pacientes, ofreciendo nuevas vías para la investigación de la salud de la mujer.

Palabras clave:
DRIVE-KGgrafo de conocimientomulti-ómicaendometriosisenfermedades complejasdescubrimiento genéticoclasificación de pacientessalud de la mujergenómicabioinformáticabiología computacional

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Área de la Ciencia:

  • Genómica y Bioinformática
  • Investigación de la Salud de la Mujer
  • Biología Computacional

Sus antecedentes:

  • La endometriosis es una afección prevalente pero subestudiada de la salud de la mujer que afecta al 10% de las mujeres en edad reproductiva.
  • La limitada caracterización genética de la endometriosis, y que los GWAS actuales explican solo el 11% de la heredabilidad, requiere enfoques integradores.
  • Las representaciones de grafos ofrecen un marco poderoso para armonizar diversos datos biológicos.

Objetivo del estudio:

  • Presentar DRIVE-KG, un novedoso grafo de conocimiento para integrar datos multiómicos para estudiar enfermedades complejas como la endometriosis.
  • Desarrollar y evaluar modelos de aprendizaje automático para la inferencia de riesgos de enfermedades y la clasificación a nivel de paciente utilizando DRIVE-KG.
  • Descubrir nuevas asociaciones de polimorfismos de un solo nucleótido (SNP)-fenotipo y mejorar las capacidades de diagnóstico para la endometriosis.

Principales métodos:

  • Se construyó un grafo heterogéneo (DRIVE-KG) que integra diversos conjuntos de datos multiómicos.
  • Se entrenó un modelo de predicción de enlaces para identificar asociaciones SNP-fenotipo (endometriosis, obesidad).
  • Se desarrolló una red convolucional de grafos (GCN) para la clasificación a nivel de paciente de endometriosis/adenomiosis.

Principales resultados:

  • Se identificaron 66 asociaciones SNP-endometriosis candidatas de alta confianza, enriquecidas para rasgos de obesidad y trastorno depresivo.
  • Se descubrieron nuevas señales genéticas para la endometriosis, distintas de las asociaciones de obesidad conocidas.
  • Se logró una puntuación F1 de 0.752 para la clasificación de endometriosis/adenomiosis utilizando GCN, superando las puntuaciones de riesgo genético (0.698).
  • GCN demostró una estratificación significativa de la gravedad de la enfermedad y la señal de adenomiosis.

Conclusiones:

  • La integración heterogénea de datos multiómicos a través de DRIVE-KG es eficaz para el descubrimiento y la predicción clínica en enfermedades subestudiadas.
  • DRIVE-KG facilita el descubrimiento de nuevas perspectivas genéticas sobre la etiología de la endometriosis.
  • El modelo GCN desarrollado muestra potencial para mejorar el diagnóstico y la estratificación de la endometriosis/adenomiosis.