Observatorio de I+D+i UPM

Memorias de investigación
Artículos en revistas:
Comparison of metaheuristics strategies for peakbin selection in proteomic mass spectrometry data
Año:2011
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
2,833
Información de impacto
Volumen
DOI
Número de revista
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0
Hasta la página
0
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Participantes
  • Autor: Ruben Armañanzas Arnedillo (UPM)
  • Autor: Maria Concepcion Bielza Lozoya (UPM)
  • Autor: Pedro Maria Larrañaga Mugica (UPM)
Grupos de investigación, Departamentos, Centros e Institutos de I+D+i relacionados
  • Creador: Grupo de Investigación: COMPUTATIONAL INTELLIGENCE GROUP
  • Departamento: Inteligencia Artificial
S2i 2021 Observatorio de investigación @ UPM con la colaboración del Consejo Social UPM
Cofinanciación del MINECO en el marco del Programa INNCIDE 2011 (OTR-2011-0236)
Cofinanciación del MINECO en el marco del Programa INNPACTO (IPT-020000-2010-22)