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Feature Selection for Microarray Gene Expression Data Using Simulated Annealing Guided by the Multivariate Joint Entropy

  • Félix Fernando González-Navarro [1] ; Lluís A. Belanche-Muñoz [2]
    1. [1] Universidad Autónoma de Baja California

      Universidad Autónoma de Baja California

      México

    2. [2] Universitat Politècnica de Catalunya

      Universitat Politècnica de Catalunya

      Barcelona, España

  • Localización: Computación y Sistemas (CyS), ISSN 1405-5546, ISSN-e 2007-9737, Vol. 18, Nº. 2, 2014, págs. 275-293
  • Idioma: inglés
  • DOI: 10.13053/CyS-18-2-2014-032
  • Títulos paralelos:
    • Selección de características para datos de expresión de los genes mediante microarreglos usando recocido simulado guiado por la entropía conjunta multivariada
  • Enlaces
  • Resumen
    • español

      La clasificación de microarreglos plantea muchos desafíos para el análisis de datos, dado que un conjunto de datos de expresión de genes puede contener docenas de observaciones con miles o incluso decenas de miles de genes. En este contexto, las técnicas de selección de subconjuntos de características pueden ser muy útiles para reducir el espacio de representación a uno manejable mediante técnicas de clasificación. En este trabajo se utiliza la entropía conjunta discretizada multivariada como base para la evaluación rápida de la relevancia de genes en el contexto de expresión génica mediante microarreglos. El algoritmo propuesto desarrolla una técnica de recocido simulado diseñada especialmente para la selección de subconjuntos de características, a través de la entropía conjunta. Esta es calculada incrementalmente, reutilizando los valores anteriores para calcular la relevancia de los subconjuntos de características. Esta combinación resulta ser una herramienta poderosa cuando se aplica a la maximización de la relevancia de un subconjunto de genes. Nuestro método ofrece soluciones altamente interpretables y más precisas que las propuestas por métodos competidores. El algoritmo propuesto es rápido, eficaz y no presenta parámetros críticos. Los resultados de los experimentos con varios conjuntos de datos de microarreglos de dominio público revelan alto rendimiento de clasificación y subconjuntos de pequeño tamaño, formados en su mayoría por genes biológicamente significativos. La técnica es general y podría ser utilizada en otros escenarios similares.

    • English

      Microarray classification poses many challenges for data analysis, given that a gene expression data set may consist of dozens of observations with thousands or even tens of thousands of genes. In this context, feature subset selection techniques can be very useful to reduce the representation space to one that is manageable by classification techniques. In this work we use the discretized multivariate joint entropy as the basis for a fast evaluation of gene relevance in a Microarray Gene Expression context. The proposed algorithm combines a simulated annealing schedule specially designed for feature subset selection with the incrementally computed joint entropy, reusing previous values to compute current feature subset relevance. This combination turns out to be a powerful tool when applied to the maximization of gene subset relevance. Our method delivers highly interpretable solutions that are more accurate than competing methods. The algorithm is fast, effective and has no critical parameters. The experimental results in several public-domain microarray data sets show a notoriously high classification performance and low size subsets, formed mostly by biologically meaningful genes. The technique is general and could be used in other similar scenarios.

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