Chemical features mining provides new descriptive structure-odor relationships
Identifieur interne : 000564 ( Main/Exploration ); précédent : 000563; suivant : 000565Chemical features mining provides new descriptive structure-odor relationships
Auteurs : Carmen C. Licon [France, États-Unis] ; Guillaume Bosc [France] ; Mohammed Sabri [France, Algérie] ; Marylou Mantel [France] ; Arnaud Fournel [France] ; Caroline Bushdid [France] ; Jerome Golebiowski [France, Corée du Sud] ; Celine Robardet [France] ; Marc Plantevit [France] ; Mehdi Kaytoue [France] ; Moustafa Bensafi [France]Source :
- PLoS Computational Biology [ 1553-734X ] ; 2019.
Descripteurs français
- KwdFr :
- MESH :
- méthodes : Biologie informatique.
- physiologie : Perception olfactive.
- Algorithmes, Bases de données chimiques, Humains, Modèles moléculaires, Odorisants, Phénomènes chimiques, Relation structure-activité.
English descriptors
- KwdEn :
- MESH :
- methods : Computational Biology.
- physiology : Olfactory Perception.
- Algorithms, Chemical Phenomena, Databases, Chemical, Humans, Models, Molecular, Odorants, Structure-Activity Relationship.
Abstract
An important goal in researching the biology of olfaction is to link the perception of smells to the chemistry of odorants. In other words, why do some odorants smell like fruits and others like flowers? While the so-called stimulus-percept issue was resolved in the field of color vision some time ago, the relationship between the chemistry and psycho-biology of odors remains unclear up to the present day. Although a series of investigations have demonstrated that this relationship exists, the descriptive and explicative aspects of the proposed models that are currently in use require greater sophistication. One reason for this is that the algorithms of current models do not consistently consider the possibility that multiple chemical rules can describe a single quality despite the fact that this is the case in reality, whereby two very different molecules can evoke a similar odor. Moreover, the available datasets are often large and heterogeneous, thus rendering the generation of multiple rules without any use of a computational approach overly complex. We considered these two issues in the present paper. First, we built a new database containing 1689 odorants characterized by physicochemical properties and olfactory qualities. Second, we developed a computational method based on a subgroup discovery algorithm that discriminated perceptual qualities of smells on the basis of physicochemical properties. Third, we ran a series of experiments on 74 distinct olfactory qualities and showed that the generation and validation of rules linking chemistry to odor perception was possible. Taken together, our findings provide significant new insights into the relationship between stimulus and percept in olfaction. In addition, by automatically extracting new knowledge linking chemistry of odorants and psychology of smells, our results provide a new computational framework of analysis enabling scientists in the field to test original hypotheses using descriptive or predictive modeling.
Url:
DOI: 10.1371/journal.pcbi.1006945
PubMed: 31022180
PubMed Central: 6504111
Affiliations:
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Le document en format XML
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<term>Chemical Phenomena (MeSH)</term>
<term>Computational Biology (methods)</term>
<term>Databases, Chemical (MeSH)</term>
<term>Humans (MeSH)</term>
<term>Models, Molecular (MeSH)</term>
<term>Odorants (MeSH)</term>
<term>Olfactory Perception (physiology)</term>
<term>Structure-Activity Relationship (MeSH)</term>
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<keywords scheme="KwdFr" xml:lang="fr"><term>Algorithmes (MeSH)</term>
<term>Bases de données chimiques (MeSH)</term>
<term>Biologie informatique (méthodes)</term>
<term>Humains (MeSH)</term>
<term>Modèles moléculaires (MeSH)</term>
<term>Odorisants (MeSH)</term>
<term>Perception olfactive (physiologie)</term>
<term>Phénomènes chimiques (MeSH)</term>
<term>Relation structure-activité (MeSH)</term>
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<keywords scheme="MESH" qualifier="méthodes" xml:lang="fr"><term>Biologie informatique</term>
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<keywords scheme="MESH" qualifier="physiologie" xml:lang="fr"><term>Perception olfactive</term>
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<keywords scheme="MESH" qualifier="physiology" xml:lang="en"><term>Olfactory Perception</term>
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<term>Bases de données chimiques</term>
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<front><div type="abstract" xml:lang="en"><p>An important goal in researching the biology of olfaction is to link the perception of smells to the chemistry of odorants. In other words, why do some odorants smell like fruits and others like flowers? While the so-called stimulus-percept issue was resolved in the field of color vision some time ago, the relationship between the chemistry and psycho-biology of odors remains unclear up to the present day. Although a series of investigations have demonstrated that this relationship exists, the descriptive and explicative aspects of the proposed models that are currently in use require greater sophistication. One reason for this is that the algorithms of current models do not consistently consider the possibility that multiple chemical rules can describe a single quality despite the fact that this is the case in reality, whereby two very different molecules can evoke a similar odor. Moreover, the available datasets are often large and heterogeneous, thus rendering the generation of multiple rules without any use of a computational approach overly complex. We considered these two issues in the present paper. First, we built a new database containing 1689 odorants characterized by physicochemical properties and olfactory qualities. Second, we developed a computational method based on a subgroup discovery algorithm that discriminated perceptual qualities of smells on the basis of physicochemical properties. Third, we ran a series of experiments on 74 distinct olfactory qualities and showed that the generation and validation of rules linking chemistry to odor perception was possible. Taken together, our findings provide significant new insights into the relationship between stimulus and percept in olfaction. In addition, by automatically extracting new knowledge linking chemistry of odorants and psychology of smells, our results provide a new computational framework of analysis enabling scientists in the field to test original hypotheses using descriptive or predictive modeling.</p>
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<li>Corée du Sud</li>
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<name sortKey="Sabri, Mohammed" sort="Sabri, Mohammed" uniqKey="Sabri M" first="Mohammed" last="Sabri">Mohammed Sabri</name>
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<country name="Corée du Sud"><noRegion><name sortKey="Golebiowski, Jerome" sort="Golebiowski, Jerome" uniqKey="Golebiowski J" first="Jerome" last="Golebiowski">Jerome Golebiowski</name>
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