Unraveling the mechanisms of action of food-derived bioactive peptides: Insights from Omics approaches
Bibliographic record
Abstract
Food-derived bioactive peptides are short protein fragments, typically containing 2–20 amino acids , that can exert physiological effects. One major challenge in this field is to unravel their underlying mechanisms of action. Therefore, this review aims to provide recent research progress on how omics can be applied to resolve this challenge. Omics, especially multi-omics, analysis is a powerful tool in identifying key altered genes, proteins, metabolites, microbial composition and their metabolites, as well as metabolic and signaling pathways . Transcriptomics , metabolomics , and microbiomics are most commonly used in bioactive peptides research in vivo and have shown vast potential in identifying their underlying causes of physiological effects in the prevention and mitigation of various chronic diseases (such as type II diabetes , obesity, hypertension, alcoholic liver disease , and neurodegenerative disorders) using respective animal models . Despite these progresses, most studies are observational and often report previously known pathways, with few focusing on elucidating new mechanistic insights. Omics analysis can be affected by many factors, such as species, age, sex, sampling (time, tissues), genetic variations, and among others. Furthermore, individual omics inherently has limitations, however when multi-omics analysis are combined and integrated, they hold the promise to unlock the mechanisms of action.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".