h(odor): Interactive Discovery of Hypotheses on the Structure-Odor Relationship in Neuroscience

Guillaume Bosc 1 Marc Plantevit 1 Jean-François Boulicaut 1 Moustafa Bensafi 2 Mehdi Kaytoue 1
1 DM2L - Data Mining and Machine Learning
LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information
2 CRNL - Centre de recherche en neurosciences de Lyon
Abstract : From a molecule to the brain perception, olfaction is a complex phenomenon that remains to be fully understood in neuroscience. Latest studies reveal that the physico-chemical properties of volatile molecules can partly explain the odor perception. Neuroscientists are then looking for new hypotheses to guide their research: physico-chemical descriptors distinguishing a subset of perceived odors. To answer this problem, we present the platform h(odor) that implements descriptive rule discovery algorithms suited for this task. Most importantly, the ol-faction experts can interact with the discovery algorithm to guide the search in a huge description space w.r.t their non-formalized background knowledge thanks to an ergonomic user interface.
Keywords : pattern mining interactive subgroup discovery
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Conference papers
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Submitted on : Tuesday, July 19, 2016 - 2:24:20 PM
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Guillaume Bosc, Marc Plantevit, Jean-François Boulicaut, Moustafa Bensafi, Mehdi Kaytoue. h(odor): Interactive Discovery of Hypotheses on the Structure-Odor Relationship in Neuroscience. ECML/PKDD 2016 (Demo), Sep 2016, Riva del Garda, Italy. pp.17--21. ⟨hal-01346679⟩

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