Brief communication: Compact cancer biomarkers discovery using a swarm intelligence feature selection algorithm

Authors:
Emmanuel Martinez;Mario Moises Alvarez;Victor Trevino
Affiliations:
Departamento de Ciencias Computacionales, Tecnologico de Monterrey Campus Monterey, Monterrey, Nuevo Leon, Mexico;Centro de Biotecnologia-FEMSA, Tecnologico de Monterrey Campus Monterey, Mexico;Departamento de Ciencias Computacionales, Tecnologico de Monterrey Campus Monterey, Monterrey, Nuevo Leon, Mexico
Venue:
Computational Biology and Chemistry
Year:
2010

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Abstract

Abstract: Biomarker discovery is a typical application from functional genomics. Due to the large number of genes studied simultaneously in microarray data, feature selection is a key step. Swarm intelligence has emerged as a solution for the feature selection problem. However, swarm intelligence settings for feature selection fail to select small features subsets. We have proposed a swarm intelligence feature selection algorithm based on the initialization and update of only a subset of particles in the swarm. In this study, we tested our algorithm in 11 microarray datasets for brain, leukemia, lung, prostate, and others. We show that the proposed swarm intelligence algorithm successfully increase the classification accuracy and decrease the number of selected features compared to other swarm intelligence methods.