ArticleMolecules (Basel, Switzerland)2023
A Novel LSTM-Based Machine Learning Model for Predicting the Activity of Food Protein-Derived Antihypertensive Peptides.
Article in Molecules (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
4 citing papers in PubMed.
- Protein hydrolysates derived from poultry by-products as nutraceuticals: Bioactive potential, limitations, safety considerations, and commercialization perspectives.Poultry science · 2026Review
- AVPpred-BWR: antiviral peptides prediction via biological words representation.Bioinformatics (Oxford, England) · 2025Article
- Research Progress of Food-Derived Antihypertensive Peptides in Regulating the Key Factors of the Renin-Angiotensin System.Nutrients · 2024Review
- Predicting Antidiabetic Peptide Activity: A Machine Learning Perspective on Type 1 and Type 2 Diabetes.International journal of molecular sciences · 2024Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Food protein-derived antihypertensive peptides are a representative type of bioactive peptides. Several models based on partial least squares regression have been constructed to delineate the relationship between the structure and activity of the peptides. Machine-learning-based models have been applied in broad areas, which also indicates their potential to be incorporated into the field of bioactive peptides. In this study, a long short-term memory (LSTM) algorithm-based deep learning model was constructed, which could predict the IC
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.