Discovering structural alerts for mutagenicity using stable emerging molecular patterns.
Identifieur interne : 000767 ( Main/Exploration ); précédent : 000766; suivant : 000768Discovering structural alerts for mutagenicity using stable emerging molecular patterns.
Auteurs : Jean-Philippe Métivier [France] ; Alban Lepailleur [France] ; Aleksey Buzmakov [France] ; Guillaume Poezevara [France] ; Bruno Crémilleux [France] ; Sergei O. Kuznetsov [Russie] ; Jérémie Le Goff [France] ; Amedeo Napoli [France] ; Ronan Bureau [France] ; Bertrand Cuissart [France]Source :
- Journal of chemical information and modeling [ 1549-960X ] ; 2015.
Descripteurs français
- KwdFr :
- MESH :
English descriptors
- KwdEn :
- MESH :
- chemical , chemistry : Mutagens.
- methods : Data Mining, Pattern Recognition, Automated.
- Drug Discovery.
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.
Url:
DOI: 10.1021/ci500611v
PubMed: 25871768
Affiliations:
- France, Russie
- Basse-Normandie, District fédéral central, Grand Est, Lorraine (région), Région Normandie
- Caen, Moscou, Nancy
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Le document en format XML
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Caen</wicri:regionArea><placeName><region type="region">Région Normandie</region><region type="old region">Basse-Normandie</region><settlement type="city">Caen</settlement></placeName></affiliation></author><author><name sortKey="Kuznetsov, Sergei O" sort="Kuznetsov, Sergei O" uniqKey="Kuznetsov S" first="Sergei O" last="Kuznetsov">Sergei O. Kuznetsov</name><affiliation wicri:level="3"><nlm:affiliation>⊥National Research University Higher School of Economics (HSE), Moscow, Russia.</nlm:affiliation><country xml:lang="fr">Russie</country><wicri:regionArea>⊥National Research University Higher School of Economics (HSE), Moscow</wicri:regionArea><placeName><settlement type="city">Moscou</settlement><region>District fédéral central</region></placeName></affiliation></author><author><name sortKey="Le Goff, Jeremie" sort="Le Goff, Jeremie" uniqKey="Le Goff J" first="Jérémie" last="Le Goff">Jérémie Le Goff</name><affiliation wicri:level="3"><nlm:affiliation>#ADn'tox, Caen, France.</nlm:affiliation><country xml:lang="fr">France</country><wicri:regionArea>#ADn'tox, Caen</wicri:regionArea><placeName><region type="region">Région Normandie</region><region type="old region">Basse-Normandie</region><settlement type="city">Caen</settlement></placeName></affiliation></author><author><name sortKey="Napoli, Amedeo" sort="Napoli, Amedeo" uniqKey="Napoli A" first="Amedeo" last="Napoli">Amedeo Napoli</name><affiliation wicri:level="3"><nlm:affiliation>∥Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), University of Lorraine, Nancy, France.</nlm:affiliation><country xml:lang="fr">France</country><wicri:regionArea>∥Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), University of Lorraine, Nancy</wicri:regionArea><placeName><region type="region">Grand Est</region><region type="old region">Lorraine (région)</region><settlement type="city">Nancy</settlement></placeName></affiliation></author><author><name sortKey="Bureau, Ronan" sort="Bureau, Ronan" uniqKey="Bureau R" first="Ronan" last="Bureau">Ronan Bureau</name><affiliation wicri:level="3"><nlm:affiliation>†Normandie Université, Caen, France.</nlm:affiliation><country xml:lang="fr">France</country><wicri:regionArea>†Normandie Université, Caen</wicri:regionArea><placeName><region type="region">Région Normandie</region><region type="old region">Basse-Normandie</region><settlement type="city">Caen</settlement></placeName></affiliation></author><author><name sortKey="Cuissart, Bertrand" sort="Cuissart, Bertrand" uniqKey="Cuissart B" first="Bertrand" last="Cuissart">Bertrand Cuissart</name><affiliation wicri:level="3"><nlm:affiliation>†Normandie Université, Caen, France.</nlm:affiliation><country xml:lang="fr">France</country><wicri:regionArea>†Normandie Université, Caen</wicri:regionArea><placeName><region type="region">Région Normandie</region><region type="old region">Basse-Normandie</region><settlement type="city">Caen</settlement></placeName></affiliation></author></analytic><series><title level="j">Journal of chemical information and modeling</title><idno type="eISSN">1549-960X</idno><imprint><date when="2015" type="published">2015</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Data Mining (methods)</term><term>Drug Discovery</term><term>Mutagens (chemistry)</term><term>Pattern Recognition, Automated (methods)</term></keywords><keywords scheme="KwdFr" xml:lang="fr"><term>Découverte de médicament</term><term>Fouille de données ()</term><term>Mutagènes ()</term><term>Reconnaissance automatique des formes ()</term></keywords><keywords scheme="MESH" type="chemical" qualifier="chemistry" xml:lang="en"><term>Mutagens</term></keywords><keywords scheme="MESH" qualifier="methods" xml:lang="en"><term>Data Mining</term><term>Pattern Recognition, Automated</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Drug Discovery</term></keywords><keywords scheme="MESH" xml:lang="fr"><term>Découverte de médicament</term><term>Fouille de données</term><term>Mutagènes</term><term>Reconnaissance automatique des formes</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">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.</div></front></TEI><affiliations><list><country><li>France</li><li>Russie</li></country><region><li>Basse-Normandie</li><li>District fédéral central</li><li>Grand Est</li><li>Lorraine (région)</li><li>Région Normandie</li></region><settlement><li>Caen</li><li>Moscou</li><li>Nancy</li></settlement></list><tree><country name="France"><region name="Région Normandie"><name sortKey="Metivier, Jean Philippe" sort="Metivier, Jean Philippe" uniqKey="Metivier J" first="Jean-Philippe" last="Métivier">Jean-Philippe Métivier</name></region><name sortKey="Bureau, Ronan" sort="Bureau, Ronan" uniqKey="Bureau R" first="Ronan" last="Bureau">Ronan Bureau</name><name sortKey="Buzmakov, Aleksey" sort="Buzmakov, Aleksey" uniqKey="Buzmakov A" first="Aleksey" last="Buzmakov">Aleksey Buzmakov</name><name sortKey="Cremilleux, Bruno" sort="Cremilleux, Bruno" uniqKey="Cremilleux B" first="Bruno" last="Crémilleux">Bruno Crémilleux</name><name sortKey="Cuissart, Bertrand" sort="Cuissart, Bertrand" uniqKey="Cuissart B" first="Bertrand" last="Cuissart">Bertrand Cuissart</name><name sortKey="Le Goff, Jeremie" sort="Le Goff, Jeremie" uniqKey="Le Goff J" first="Jérémie" last="Le Goff">Jérémie Le Goff</name><name sortKey="Lepailleur, Alban" sort="Lepailleur, Alban" uniqKey="Lepailleur A" first="Alban" last="Lepailleur">Alban Lepailleur</name><name sortKey="Napoli, Amedeo" sort="Napoli, Amedeo" uniqKey="Napoli A" first="Amedeo" last="Napoli">Amedeo Napoli</name><name sortKey="Poezevara, Guillaume" sort="Poezevara, Guillaume" uniqKey="Poezevara G" first="Guillaume" last="Poezevara">Guillaume Poezevara</name></country><country name="Russie"><region name="District fédéral central"><name sortKey="Kuznetsov, Sergei O" sort="Kuznetsov, Sergei O" uniqKey="Kuznetsov S" first="Sergei O" last="Kuznetsov">Sergei O. Kuznetsov</name></region></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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