Algoritmos Evolutivos Multiobjetivo aplicados a la Selección de Características en Microarrays de Datos de Cáncer

• Dussaut, Julieta Sol ^[1] ; Ponzoni, Ignacio ^[1] ; Olivera, Ana Carolina ^[2] ; Vidal, Pablo Javier ^[2]
  1. [1] Universidad Nacional del Sur
  2. [2] Universidad Nacional de Cuyo
• Localización: Entre ciencia e ingeniería, ISSN-e 2539-4169, ISSN 1909-8367, Vol. 14, Nº. 28, 2020, págs. 40-45
• Idioma: español
• DOI: 10.31908/19098367.2014
• Títulos paralelos:
  • Multiobjective Evolutionary Algorithms applied to Feature Selection in Microarrays Cancer Data
• Enlaces
  • Texto completo
• Resumen
  • español

    El análisis de microarrays de expresión de genes es un tópico actual para el diagnóstico y clasificación del cáncer humano. Un microarray de datos de expresión de genes consiste en una matriz de miles de características de las cuales la mayoría es irrelevante para clasificar patrones de expresiones de genes. La elección de un subconjunto mínimo de características para clasificación es una tarea dificultosa. En este trabajo, se realiza una comparación entre dos algoritmos evolutivos multiobjetivo aplicados a conjuntos de expresiones de genes populares en la literatura (linfoma, leucemia y colon). Con el objetivo de remover las características con fuerte correlación se realiza una etapa de pre-procesamiento. Se muestra un análisis extenso y detallado de los resultados obtenidos para los algoritmos multiobjetivo seleccionados.

  • English

    Microarray analysis of gene expression is a current topic for the diagnosis and classification of human cancer. A gene expression data microarray consists of an array of thousands of features of which most are irrelevant for classifying patterns of gene expressions. Choosing a minimal subset of features for classification is a difficult task. In this work, a comparison is made between two multi-objective evolutionary algorithms applied to sets of gene expressions popular in the literature (lymphoma, leukemia and colon). In order to remove the strongly correlated characteristics, a pre-processing stage is performed. An extensive and detailed analysis of the results obtained for the selected multi-objective algorithms is shown.

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