title: Automated detection of structural alerts (chemical fragments) in (eco)toxicology.
abstract: This mini-review describes the evolution of different algorithms dedicated to the automated discovery of chemical fragments associated to (eco)toxicological endpoints. These structural alerts correspond to one of the most interesting approach of in silico toxicology due to their direct link with specific toxicological mechanisms. A number of expert systems are already available but, since the first work in this field which considered a binomial distribution of chemical fragments between two datasets, new data miners were developed and applied with success in chemoinformatics. The frequency of a chemical fragment in a dataset is often at the core of the process for the definition of its toxicological relevance. However, recent progresses in data mining provide new insights into the automated discovery of new rules. Particularly, this review highlights the notion of Emerging Patterns that can capture contrasts between classes of data.
authors.affiliation: Normandie Univ, France ; UNICAEN, CERMN (Centre d'Etudes et de Recherche sur le Médicament de Normandie, FR CNRS INC3M - SF ICORE, Université de Caen Basse- Normandie, U.F.R. des Sciences Pharmaceutiques), F-14032 Caen, France.
authors.forename: Alban
authors.initials: A
authors.lastname: Lepailleur
authors.affiliation: Normandie Univ, France ; UNICAEN, GREYC (Groupe de Recherche en Informatique, Image, Automatique et Instrumentation de Caen, CNRS UMR 6072, Université de Caen Basse-Normandie), F-14032 Caen, France.
authors.forename: Guillaume
authors.initials: G
authors.lastname: Poezevara
authors.affiliation: Normandie Univ, France ; UNICAEN, CERMN (Centre d'Etudes et de Recherche sur le Médicament de Normandie, FR CNRS INC3M - SF ICORE, Université de Caen Basse- Normandie, U.F.R. des Sciences Pharmaceutiques), F-14032 Caen, France.
authors.forename: Ronan
authors.initials: R
authors.lastname: Bureau