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e-Bitter: Bitterant Prediction by the Consensus Voting From the Machine-Learning Methods
In-silico bitterant prediction received the considerable attention due to the expensive and laborious experimental-screening of the bitterant. In this work, we collect the fully experimental dataset containing 707 bitterants and 592 non-bitterants, which is distinct from the fully or partially hypot...
| Autores principales: | , , , , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Frontiers Media S.A.
2018
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5885771/ https://www.ncbi.nlm.nih.gov/pubmed/29651416 http://dx.doi.org/10.3389/fchem.2018.00082 |