title: ANVAS: artificial neural variables adaptation system for descriptor selection.
abstract: A new algorithm model-oriented for variable selection is presented in this study. It is based on the combination of genetic algorithms (GA) for hyperspace exploration, and counterpropagation artificial neural network (CP ANN) for deriving the fitness score. The proposed method performed very well on both well defined synthetic data sets and real academic data sets.
authors.affiliation: Istituto Mario Negri, via Eritrea 62, 20157 Milan, Italy. mazzatorta@marionegri.it
authors.forename: Paolo
authors.initials: P
authors.lastname: Mazzatorta
authors.forename: Marjan
authors.initials: M
authors.lastname: Vracko
authors.forename: Emilio
authors.initials: E
authors.lastname: Benfenati
mesh_terms: D000465:Algorithms; D002273:Carcinogens / Q000737:chemistry / Q000494:pharmacology; D002875:Chromosomes / Q000235:genetics; D003198:Computer Simulation; D019143:Evolution, Molecular; D005838:Genotype; D008957:Models, Genetic; D009153:Mutagens / Q000737:chemistry / Q000494:pharmacology; D009154:Mutation / Q000187:drug effects / Q000235:genetics; D016571:Neural Networks, Computer; D010641:Phenotype; D025341:Principal Component Analysis; D021281:Quantitative Structure-Activity Relationship; D012044:Regression Analysis; D012641:Selection, Genetic
chemical_list: D002273:Carcinogens; D009153:Mutagens