@inproceedings{CI-grenier-2013,


hal_id = {hal-00824172},
title = {{Treelet Kernel Incorporating Chiral Information}},
author = {Grenier, Pierre-Anthony and Brun, Luc and Villemin, Didier},
abstract = {{Molecules being often described using a graph representation, graph kernels provide an interesting framework which allows to combine machine learning and graph theory in order to predict molecule's properties. However, some of these properties are induced both by relationships between the atoms of a molecule and by constraints on the relative positioning of these atoms. Graph kernels based solely on the graph representation of a molecule do not encode this relative positioning of atoms and are consequently unable to predict accurately some molecule's properties. This paper presents a new method which incorporates spatial constraints into the graph kernel framework in order to overcome this limitation.}},
keywords = {Graph kernel; Chemoinformatics; Chirality},
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 = {{9th IAPR-TC15 International Workshop on Graph-based Representations in Pattern Recognition}},
address = {Vienne, Autriche},
audience = {internationale },
year = {2013},
month = May,
url = {Abstact:=http://hal.archives-ouvertes.fr/hal-00824172,PDF:=http://hal.archives-ouvertes.fr/hal-00824172/PDF/article.pdf},
theme="pattern,chemo"

 }