ArticleFoods (Basel, Switzerland)2021
A Novel Machine Learning Strategy for the Prediction of Antihypertensive Peptides Derived from Food with High Efficiency.
Article in Foods (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 34 citations in OpenAlex.
- Food-Derived Multi-Target Antihypertensive Peptides: Sources, Mechanisms and AI-Driven Strategies.Foods (Basel, Switzerland) · 2026Review
- Predicting Antidiabetic Peptide Activity: A Machine Learning Perspective on Type 1 and Type 2 Diabetes.International journal of molecular sciences · 2024Article
- A comparative study on bath and horn ultrasound-assisted modification of bentonite and their effects on the bleaching efficiency of soybean and sunflower oil: Machine learning as a new approach for mathematical modeling.Food science & nutrition · 2024Article
- Peptide-based drug discovery through artificial intelligence: towards an autonomous design of therapeutic peptides.Briefings in bioinformatics · 2024Review
- Mining Bovine Milk Proteins for DPP-4 Inhibitory Peptides Using Machine Learning and Virtual Proteolysis.Research (Washington, D.C.) · 2024Article
- A Novel LSTM-Based Machine Learning Model for Predicting the Activity of Food Protein-Derived Antihypertensive Peptides.Molecules (Basel, Switzerland) · 2023Article
- Revealing the Sequence Characteristics and Molecular Mechanisms of ACE Inhibitory Peptides by Comprehensive Characterization of 160,000 Tetrapeptides.Foods (Basel, Switzerland) · 2023Article
- Identification of Distinct Characteristics of Antibiofilm Peptides and Prospection of Diverse Sources for Efficacious Sequences.Frontiers in microbiology · 2021Article
- Integration of Machine Learning and Structural Analysis for Predicting Peptide Antibiofilm Effects: Advancements in Drug Discovery for Biofilm-Related Infections.Iranian journal of pharmaceutical research : IJPRArticle
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
Abstract
Strategies to screen antihypertensive peptides with high throughput and rapid speed will doubtlessly contribute to the treatment of hypertension. Food-derived antihypertensive peptides can reduce blood pressure without side effects. In the present study, a novel model based on the eXtreme Gradient Boosting (XGBoost) algorithm was developed and compared with the dominating machine learning models. To further reflect on the reliability of the method in a real situation, the optimized XGBoost model was utilized to predict the antihypertensive degree of the k-mer peptides cutting from six key proteins in bovine milk, and the peptide-protein docking technology was introduced to verify the findings. The results showed that the XGBoost model achieved outstanding performance, with an accuracy of 86.50% and area under the receiver operating characteristic curve of 94.11%, which were better than the other models. Using the XGBoost model, the prediction of antihypertensive peptides derived from milk protein was consistent with the peptide-protein docking results, and was more efficient. Our results indicate that using the XGBoost algorithm as a novel auxiliary tool is feasible to screen for antihypertensive peptides derived from food, with high throughput and high efficiency.
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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.