Memorias de investigación
Research Publications in journals:
Comparison of metaheuristic strategies for peakbin selection in proteomic mass spectrometry data
Year:2013
Research Areas
  • Artificial intelligence
Information
Abstract
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.
International
Si
JCR
Si
Title
Information Sciences
ISBN
0020-0255
Impact factor JCR
2,833
Impact info
Datos JCR del año 2010
Volume
222
10.1016/j.ins.2010.12.013
Journal number
From page
229
To page
246
Month
FEBRERO
Ranking
Participants
Research Group, Departaments and Institutes related
  • Creador: Departamento: Inteligencia Artificial