title: Compact cancer biomarkers discovery using a swarm intelligence feature selection algorithm.
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.
authors.affiliation: Departamento de Ciencias Computacionales, Tecnologico de Monterrey Campus Monterey, Monterrey, Nuevo Leon, Mexico.
authors.forename: Emmanuel
authors.initials: E
authors.lastname: Martinez
authors.forename: Mario Moises
authors.initials: MM
authors.lastname: Alvarez
authors.forename: Victor
authors.initials: V
authors.lastname: Trevino
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D014408:Biomarkers, Tumor / Q000235:genetics*; D020869:Gene Expression Profiling / Q000379:methods*; D023281:Genomics / Q000379:methods; D009369:Neoplasms / Q000175:diagnosis* / Q000235:genetics; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*
chemical_list: D014408:Biomarkers, Tumor