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A Machine Learning Method to Identify Umami Peptide Sequences by Using Multiplicative LSTM Embedded Features
Umami peptides enhance the umami taste of food and have good food processing properties, nutritional value, and numerous potential applications. Wet testing for the identification of umami peptides is a time-consuming and expensive process. Here, we report the iUmami-DRLF that uses a logistic regres...
| Autores principales: | , , , , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
MDPI
2023
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10094688/ https://www.ncbi.nlm.nih.gov/pubmed/37048319 http://dx.doi.org/10.3390/foods12071498 |