Protein coding genes as hosts for noncoding RNA expression
Bibliographic record
Abstract
With the emergence of high-throughput sequence characterization methods and the subsequent improvements in gene annotations, it is becoming increasingly clear that a large proportion of eukaryotic protein-coding genes (as many as 50% in human) serve as host genes for non-coding RNA genes. Amongst the most extensively characterized embedded non-coding RNA genes, small nucleolar RNAs and microRNAs represent abundant families. Encoded individually or clustered, in sense or antisense orientation with respect to their host and independently expressed or dependent on host expression, the genomic characteristics of embedded genes determine their biogenesis and the extent of their relationship with their host gene. Not only can host genes and the embedded genes they harbour be co-regulated and mutually modulate each other, many are functionally coupled playing a role in the same cellular pathways. And while host-non-coding RNA relationships can be highly conserved, mechanisms have been identified, and in particular an association with transposable elements, allowing the appearance of copies of non-coding genes nested in host genes, or the migration of embedded genes from one host gene to another. The study of embedded non-coding genes and their relationship with their host genes increases the complexity of cellular networks and provides important new regulatory links that are essential to properly understand cell function.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.005 |
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".