title: Discovering structural alerts for mutagenicity using stable emerging molecular patterns.
abstract: This study is dedicated to the introduction of a novel method that automatically extracts potential structural alerts from a data set of molecules. These triggering structures can be further used for knowledge discovery and classification purposes. Computation of the structural alerts results from an implementation of a sophisticated workflow that integrates a graph mining tool guided by growth rate and stability. The growth rate is a well-established measurement of contrast between classes. Moreover, the extracted patterns correspond to formal concepts; the most robust patterns, named the stable emerging patterns (SEPs), can then be identified thanks to their stability, a new notion originating from the domain of formal concept analysis. All of these elements are explained in the paper from the point of view of computation. The method was applied to a molecular data set on mutagenicity. The experimental results demonstrate its efficiency: it automatically outputs a manageable number of structural patterns that are strongly related to mutagenicity. Moreover, a part of the resulting structures corresponds to already known structural alerts. Finally, an in-depth chemical analysis relying on these structures demonstrates how the method can initiate promising processes of chemical knowledge discovery.
authors: †Normandie Université, Caen, France.|‡UNICAEN, GREYC, UMR CNRS 6072, F-14032 Caen, France.
authors: Jean-Philippe
authors: JP
authors: Métivier
authors: †Normandie Université, Caen, France.|§UNICAEN, CERMN, UPRES EA 4258, FR CNRS 3038, F-14032 Caen, France.
authors: Alban
authors: A
authors: Lepailleur
authors: ∥Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), University of Lorraine, Nancy, France.|⊥National Research University Higher School of Economics (HSE), Moscow, Russia.
authors: Aleksey
authors: A
authors: Buzmakov
authors: †Normandie Université, Caen, France.|‡UNICAEN, GREYC, UMR CNRS 6072, F-14032 Caen, France.|§UNICAEN, CERMN, UPRES EA 4258, FR CNRS 3038, F-14032 Caen, France.
authors: Guillaume
authors: G
authors: Poezevara
authors: †Normandie Université, Caen, France.|‡UNICAEN, GREYC, UMR CNRS 6072, F-14032 Caen, France.
authors: Bruno
authors: B
authors: Crémilleux
authors: ⊥National Research University Higher School of Economics (HSE), Moscow, Russia.
authors: Sergei O
authors: SO
authors: Kuznetsov
authors: #ADn'tox, Caen, France.
authors: Jérémie
authors: J
authors: Le Goff
authors: ∥Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), University of Lorraine, Nancy, France.
authors: Amedeo
authors: A
authors: Napoli
authors: †Normandie Université, Caen, France.|§UNICAEN, CERMN, UPRES EA 4258, FR CNRS 3038, F-14032 Caen, France.
authors: Ronan
authors: R
authors: Bureau
authors: †Normandie Université, Caen, France.|‡UNICAEN, GREYC, UMR CNRS 6072, F-14032 Caen, France.
authors: Bertrand
authors: B
authors: Cuissart
mesh_terms: D057225:Data Mining / Q000379:methods*; D055808:Drug Discovery; D009153:Mutagens / Q000737:chemistry*; D010363:Pattern Recognition, Automated / Q000379:methods*
chemical_list: D009153:Mutagens