Competing Endogenous RNAs (ceRNAs) and Application of Their Regulatory Networks in Complex Traits and Diseases of Ruminants
Bibliographic record
Abstract
This manuscript summarizes information on the diverse range of RNA molecules and their role as competing endogenous RNAs (ceRNAs). Moreover, it provides an overview of ceRNA regulatory networks and their applications in ruminant biology. Knowledge of co-expression networks has increased with microarrays, RNA-seq, and scRNA-seq characterizing molecular mediators across various biological scales, using sequences from numerous blood and tissue samples. By synthesizing existing knowledge, this study summarizes interactions between coding and non-coding RNAs through microRNA response elements (MREs), elucidating large-scale regulatory networks throughout the transcriptome that influence the expression and activities of various ceRNAs. Identification of non-coding RNAs with important regulatory functions will revolutionize understanding of RNA biology, shifting from an mRNA-centric model to a complex network of RNA crosstalk. The ceRNA networks offer a more comprehensive and arguably more realistic perspective compared to protein–protein interaction (PPI) networks and weighted gene co-expression networks (WGCN). These ceRNA regulatory networks can describe potential molecular regulatory mechanisms related to functional and economically important traits in ruminants, plus contribute to disease and pathology research, by elucidating pathogenesis and potential drug effects in disease and cancer models. Furthermore, they can provide insights into farm animal biology, e.g., reproductive traits in goats and sheep, regulation of fat metabolism in beef cattle, heat stress responses, and lactation regulation in dairy cattle, fertility and muscle characteristics in buffalo, and resistance to high-salt and water-deprivation conditions in camels. In conclusion, ceRNA and associated regulatory networks should promote a new understanding of molecular mechanisms and identify candidate genes and metabolic-signaling pathways in ruminants.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".