ArticleFoods (Basel, Switzerland)2023
A Machine Learning Method to Identify Umami Peptide Sequences by Using Multiplicative LSTM Embedded Features.
Article in Foods (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Identification and Sensory Characterization of Umami Peptides During Lager Beer Fermentation.Foods (Basel, Switzerland) · 2026Article
- Machine learning unveils three layers of food complexity.NPJ science of food · 2026Review
- In silico discovery of umami peptides: From sequence-based intelligent models to structure-function mechanistic insight.Current research in food science · 2026Review
- TastepepAI: An artificial intelligence platform for taste peptide de novo design.PLoS computational biology · 2025Article
- In Silico Discovery and Sensory Validation of Umami Peptides in Fermented Sausages: A Study Integrating Deep Learning and Molecular Modeling.Foods (Basel, Switzerland) · 2025Article
- AOPxSVM: A Support Vector Machine for Identifying Antioxidant Peptides Using a Block Substitution Matrix and Amino Acid Composition, Transformation, and Distribution Embeddings.Foods (Basel, Switzerland) · 2025Article
- FEOpti-ACVP: identification of novel anti-coronavirus peptide sequences based on feature engineering and optimization.Briefings in bioinformatics · 2024Article
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Authors and funding
7 authors.
Funding
Abstract
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 regression (LR) method solely based on the deep learning pre-trained neural network feature extraction method, unified representation (UniRep based on multiplicative LSTM), for feature extraction from the peptide sequences. The findings demonstrate that deep learning representation learning significantly enhanced the capability of models in identifying umami peptides and predictive precision solely based on peptide sequence information. The newly validated taste sequences were also used to test the iUmami-DRLF and other predictors, and the result indicates that the iUmami-DRLF has better robustness and accuracy and remains valid at higher probability thresholds. The iUmami-DRLF method can aid further studies on enhancing the umami flavor of food for satisfying the need for an umami-flavored diet.
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Registered trials
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