H(odor): Interactive Discovery of Hypotheses on the Structure-Odor Relationship in Neuroscience
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 olfaction 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.
Cite
Text
Bosc et al. "H(odor): Interactive Discovery of Hypotheses on the Structure-Odor Relationship in Neuroscience." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2016. doi:10.1007/978-3-319-46131-1_4Markdown
[Bosc et al. "H(odor): Interactive Discovery of Hypotheses on the Structure-Odor Relationship in Neuroscience." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2016.](https://mlanthology.org/ecmlpkdd/2016/bosc2016ecmlpkdd-odor/) doi:10.1007/978-3-319-46131-1_4BibTeX
@inproceedings{bosc2016ecmlpkdd-odor,
title = {{H(odor): Interactive Discovery of Hypotheses on the Structure-Odor Relationship in Neuroscience}},
author = {Bosc, Guillaume and Plantevit, Marc and Boulicaut, Jean-François and Bensafi, Moustafa and Kaytoue, Mehdi},
booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
year = {2016},
pages = {17-21},
doi = {10.1007/978-3-319-46131-1_4},
url = {https://mlanthology.org/ecmlpkdd/2016/bosc2016ecmlpkdd-odor/}
}