title: A comparison of four feature selection algorithms applied to multiply-imputed proteomic data.
abstract: Missing data are often imputed for analysis, but a single imputation may be inaccurate when performing feature selection in mining data. Feature selection procedures applied to multiply imputed data demonstrate this phenomenon and suggest that multiple imputation is an important adjunct to knowledge discovery.
authors.affiliation: University of Pennsylvania School of Medicine, Philadelphia, PA, USA.
authors.forename: John H
authors.initials: JH
authors.lastname: Holmes
authors.forename: Malek
authors.initials: M
authors.lastname: Kamoun
authors.forename: Ajay
authors.initials: A
authors.lastname: Israni
authors.forename: Marshall
authors.initials: M
authors.lastname: Joffe
authors.forename: Wei
authors.initials: W
authors.lastname: Yang
authors.forename: Harold I
authors.initials: HI
authors.lastname: Feldman
mesh_terms: D000465:Algorithms; D003628:Database Management Systems; D016208:Databases, Factual; D040901:Proteomics