title: QSAR in ecotoxicity: an overview of modern classification techniques.
abstract: This study deals with classification for toxicity prediction. Using a data set of 235 pesticides and 153 descriptors, we built several models using seven classification algorithms: nearest mean classifier, linear discriminant analysis, quadratic discriminant analysis, regularized discriminant analysis, soft independent modeling of class analogy, K nearest neighbors classification, classification, and regression tree. The performance of the models was then compared with the classifier, the end-points, the number of descriptor, and the diversity of the data set. Finally, we made a critical analysis of the models and descriptors.
authors.affiliation: Istituto di Ricerche Farmacologiche Mario Negri Milano, Via Eritrea, 62, 20157 Milano, Italy. mazzatorta@marionegri.it
authors.forename: Paolo
authors.initials: P
authors.lastname: Mazzatorta
authors.forename: Emilio
authors.initials: E
authors.lastname: Benfenati
authors.forename: Paola
authors.initials: P
authors.lastname: Lorenzini
authors.forename: Marco
authors.initials: M
authors.lastname: Vighi
mesh_terms: D000465:Algorithms; D004463:Ecology; D010575:Pesticides / Q000633:toxicity*; D021281:Quantitative Structure-Activity Relationship
chemical_list: D010575:Pesticides