Characterization of the microRNA pool and the factors affecting its regulatory potential
Bibliographic record
Abstract
The regulation of gene expression by microRNAs (miRNAs) is complex due to a number of variables involved. The potential for one miRNA to target many genes, the presence of multiple miRNA response elements (MREs) in one mRNA molecule and the interplay between RNAs that share common MREs each add a layer of complexity to the process; making it difficult to determine how regulation of gene expression by miRNAs works within the context of the system as a whole. In this study, we used luciferase report vectors inserted with different 3'UTR fragments as probes to detect the repressive effect of the miRNA pool on gene expression and uncovered some essential characteristics of gene regulation mediated by the miRNA pool, such as the nonlinear correlative relationship between the regulatory potential of a miRNA pool and the number of potential MREs, the buffering effect and the saturating effect of the miRNA pool, and the restrictive effect caused by the density of MREs. Through expressing gradient concentration of 3'UTR fragments, we indirectly detected the regulatory potential of the competing endogenous RNA (ceRNA) pool and analysed its effect on the regulatory potential of the miRNA pool. Our results provide some new insights into miRNA pool mediated gene regulation.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".