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Memorias de investigación
Artículos en revistas:
Comparison of metaheuristic strategies for peakbin selection in proteomic mass spectrometry data
Año:2013
Áreas de investigación
  • Inteligencia artificial
Datos
Descripción
Mass spectrometry (MS) data provide a promising strategy for biomarker discovery. For this purpose, the detection of relevant peakbins in MS data is currently under intense research. Data from mass spectrometry are challenging to analyze because of their high dimensionality and the generally low number of samples available. To tackle this problem, the scientific community is becoming increasingly interested in applying feature subset selection techniques based on specialized machine learning algorithms. In this paper, we present a performance comparison of some metaheuristics: best first (BF), genetic algorithm (GA), scatter search (SS) and variable neighborhood search (VNS). Up to now, all the algorithms, except for GA, have been first applied to detect relevant peakbins in MS data. All these metaheuristic searches are embedded in two different filter and wrapper schemes coupled with Naive Bayes and SVM classifiers.
Internacional
Si
JCR del ISI
Si
Título de la revista
Information Sciences
ISSN
0020-0255
Factor de impacto JCR
3,643
Información de impacto
Volumen
222
DOI
Número de revista
Desde la página
229
Hasta la página
246
Mes
SIN MES
Ranking
Esta actividad pertenece a memorias de investigación
Participantes
  • Autor: M. García-Torres
  • Autor: R. Armañanzas
  • Autor: Maria Concepcion Bielza Lozoya (UPM)
  • Autor: P. Larrañaga
Grupos de investigación, Departamentos, Centros e Institutos de I+D+i relacionados
  • Creador: Departamento: Inteligencia Artificial
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