Bibliographic record
Abstract
C ardiac development is anchored on an intricate program of gene regulation and coordination, associated with critical timing and cell-cell interactions.Rather than a single master regulatory process, as originally envisioned to reside in a transcriptional complex or protein signaling cascade, cardiac development is likely regulated by a network of coordinated gene expressions, critically timed, and calibrated.Noncoding RNAs (ncRNA) are now seen as key new players in this regulatory network, and the road map is only beginning to be constructed. Article see p 110Cardiovascular diseases often recapitulate cardiac development when the cardiovascular systems sustain stress or injury.Searches for disease-associated genes often ended up in the noncoding intergenic regions of the genome where ncRNAs are often expressed (eg, 9p21 chromosomal region for atherosclerosis).Thus, the role of ncRNAs in the pathogenesis of diseases has assumed an increasing importance, even though the full mechanistic understanding is yet to evolve.Long noncoding RNAs (lncRNA) are RNA transcripts longer than 200 nucleotides expressed by the genome, but do not themselves code proteins.They are transcribed across the genome, including the intergenic regions as potentially overlapping sense and antisense transcripts that can flank proteincoding genes.Coordinated activities of lcnRNAs likely play a major role in the regulatory networks of organ development, normal organ function, and disease pathogenesis.Until recently, ncRNAs were considered generic regulators of cell function, controlling the basic pathways of mRNA splicing and protein translation.However, in the past 10 years large scale community projects such as ENCODE (Encycopdia of DNA elements) or FANTOM (Functional Annotation of the Mammalian Genome) have taught us that ncRNAs outnumber protein-coding genes by nearly 40:1 and play integrated and specific roles in controlling gene expression and function.1 NcRNAs are broadly classified as long (lncRNA, >200 bp), small (microRNA or miRNA, piRNA), and regulatory (rRNA, (
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".