title: Guilt-By-Association feature selection applied to simulated proteomic data.
abstract: We propose a new feature selection algorithm, Guilt-By-Association (GBA), which uses hierarchical clustering based on feature correlations to eliminate redundant features. GBA can be used in conjunction with other algorithms to produce a feature selection routine that explicitly considers both the similarities between features and their individual discriminatory powers. In this preliminary study, a simple form of GBA was investigated on simulated proteomic data.
authors.affiliation: Department of Electrical and Computer Engineering, The University of Texas at Austin, TX, USA.
authors.forename: Hyunjin
authors.initials: H
authors.lastname: Shin
authors.forename: Bryan
authors.initials: B
authors.lastname: Sheu
authors.forename: Mia K
authors.initials: MK
authors.lastname: Markey
mesh_terms: D000465:Algorithms; D015415:Biomarkers / Q000032:analysis; D013058:Mass Spectrometry; D020543:Proteome
chemical_list: D015415:Biomarkers; D020543:Proteome