@inproceedings{CI-gauzere-2013-2,


hal_id = {hal-00829227},
title = {{Relevant Cycle Hypergraph Representation for Molecules}},
author = {Ga{"u}z{`e}re, Benoit and Brun, Luc and Villemin, Didier},
abstract = {Chemoinformatics aims to predict molecule's properties through informational methods. Some methods base their prediction model on the comparison of molecular graphs. Considering such a molecular representation, graph kernels provide a nice framework which allows to combine machine learning techniques with graph theory. Despite the fact that molecular graph encodes all structural information of a molecule, it does not explicitly encode cyclic information. In this paper, we propose a new molecular representation based on a hypergraph which explicitly encodes both cyclic and acyclic information into one molecular representation called relevant cycle hypergraph. In addition, we propose a similarity measure in order to compare relevant cycle hypergraphs and use this molecular representation in a chemoinformatics prediction problem.},
language = {Anglais},
affiliation = {Groupe de Recherche en Informatique, Image, Automatique et Instrumentation de Caen - GREYC , Laboratoire de chimie mol{'e}culaire et thioorganique - LCMT},
booktitle = {Graph-Based Representations in Pattern Recognition},
pages = {111},
address = {Autriche},
audience = {internationale },
year = {2013},
month = May,
url = {Abstract:= http://hal.archives-ouvertes.fr/hal-00829227,pdf:=http://hal.archives-ouvertes.fr/hal-00829227/PDF/GbR2013_014.pdf},
theme="pattern",

 }