Comparing molecular representations, e-nose signals, and other featurization, for learning to smell aroma molecules

Recent research has attempted to predict our perception of odorants using Machine Learning models. The featurization of the olfactory stimuli usually represents the odorants using molecular structure parameters, molecular fingerprints, mass spectra, or e-nose signals. However, the impact of the choi...

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Detalles Bibliográficos
Autores principales: Debnath, Tanoy, Badreddine, Samy, Kumari, Priyadarshini, Spranger, Michael
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2023
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10420360/
https://www.ncbi.nlm.nih.gov/pubmed/37566580
http://dx.doi.org/10.1371/journal.pone.0289881

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10420360/
https://www.ncbi.nlm.nih.gov/pubmed/37566580
http://dx.doi.org/10.1371/journal.pone.0289881

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