MicroRNA expression profiles across blood and different tissues in cattle
Bibliographic record
Abstract
MicroRNAs (miRNAs) play essential roles in regulating gene expression involved in various biological functions. The knowledge of miRNA expression profiles across different tissues in cattle is still limited. Using the miRNAs data generated from 158 samples in three studies, we characterized the miRNA expression profiles of bovine sera, exosomes and 11 different tissues. Totally 639 miRNAs were identified and 159 miRNAs were expressed in all samples. After relative log expression normalization, four miRNA expression clusters were generated: 1) sera and exosomes; 2) liver; 3) mammary gland; 4) rumen and gut tissues. The top 10 most abundant miRNAs accounted for >55% of total miRNA expression in each tissue. In addition, this study described a detailed pipeline for identification of both tissue and circulating miRNAs, and the shareable datasets can be re-used by researchers to investigate miRNA-related biological questions in cattle. In addition, a web-based repository was developed, which enables researchers to access the distribution range and raw counts number of the miRNA expression data (https://www.cattleomics.com/micrornaome).
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".