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Detectar nuevas asociaciones en grandes conjuntos de datos.

David N Reshef1, Yakir A Reshef, Hilary K Finucane

  • 1Department of Computer Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA. dnreshef@mit.edu

Science (New York, N.Y.)
|December 17, 2011
PubMed
Resumen
Este resumen es generado por máquina.

Introducimos el coeficiente máximo de información (MIC), una nueva medida para encontrar relaciones variables en grandes conjuntos de datos. MIC identifica diversas asociaciones, lo que resulta útil en varios campos científicos.

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Área de la Ciencia:

  • Estadísticas Estadísticas Las estadísticas.
  • Minería de datos Minería de datos
  • La bioinformática es la bioinformática.

Sus antecedentes:

  • La identificación de relaciones complejas entre variables en grandes conjuntos de datos es crucial para el descubrimiento científico.
  • Los métodos existentes pueden no capturar todo el espectro de asociaciones, incluidas las no lineales y no funcionales.

Objetivo del estudio:

  • Introducir una nueva medida estadística, el coeficiente máximo de información (MIC), para cuantificar las relaciones entre dos variables.
  • Para demostrar la utilidad de MIC y las estadísticas más amplias de exploración no paramétrica basadas en información máxima (MINE) en diversos conjuntos de datos.

Principales métodos:

  • El coeficiente máximo de información (MIC) se desarrolló como una medida de dependencia para pares de variables.
  • MIC es parte del conjunto de estadísticas de exploración no paramétrica (MINE) basado en la información máxima.
  • Las estadísticas de MIC y MINE se aplicaron a conjuntos de datos del mundo real.

Principales resultados:

  • MIC captura efectivamente una amplia gama de asociaciones, incluidas las relaciones funcionales y no funcionales.
  • Para las relaciones funcionales, las puntuaciones de MIC se aproximan al coeficiente de determinación (R(2)).
  • Aplicación a la salud global, la expresión génica, el béisbol y los datos del microbioma revelaron relaciones conocidas y nuevas.

Conclusiones:

  • MIC proporciona una herramienta poderosa y versátil para explorar dependencias variables en grandes conjuntos de datos.
  • El marco MINE ofrece un enfoque robusto para la exploración de datos y el descubrimiento de relaciones.
  • MIC y MINE tienen una amplia aplicabilidad en varios dominios científicos para identificar patrones significativos.