NN-PRED: Un nuevo programa para la predicción de la estructura secundaria de la proteína usando redes neuronales

  • Oscar Fernando Bedoya Leiva Universidad del Valle
  • Eduard Alberto Satizábal Tascón Universidad del Valle

Resumen

En este artículo se propone un nuevo programa para la predicción de la estructura secundaria de la proteína. El programa, llamado NN-Pred, recibe como entrada múltiples secuencias de ADN y utiliza cinco algoritmos existentes para la predicción de la estructura secundaria de la proteína. Además, NN-Pred calcula una predicción consenso que se basa en una estrategia de redes neuronales y que se plantea en este artículo para mejorar la exactitud en la predicción. NN-Pred se obtuvo usando una metodología conocida como estrategia consenso que intenta obtener un modelo de predicción integrando algunos de los mejores métodos existentes. El programa NN-Pred provee una predicción de tres estados (hélices alfa, hojas beta, y otro) para la estructura secundaria de la proteína. De acuerdo a los resultados de las pruebas realizadas, NN-Pred alcanza una exactitud de predicción de al menos 70.0%, sobrepasando la mayoría de los métodos existentes. Los experimentos realizados mostraron que la técnica de redes neuronales se puede usar como una estrategia consenso para obtener modelos precisos para la predicción de la estructura secundaria.

Palabras clave: secondary structure prediction, consensus strategy, neural networks

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Biografía del autor

Oscar Fernando Bedoya Leiva, Universidad del Valle

Magíster en Ingeniería de Sistemas. Docente de la Escuela de Ingeniería de Sistemas y Computación.

Eduard Alberto Satizábal Tascón, Universidad del Valle

Ingeniero de Sistemas.

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Publicado
2013-03-09

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