Regulatory RNAs: Future Perspectives in Diagnosis, Prognosis, and Individualized Therapy
Bibliographic record
Abstract
With potentially up to 1000 microRNAs (miRNAs) present in the human genome, altogether regulating the expression of thousands of genes, one can anticipate that miRNAs will play a significant role in health and disease. Deregulated protein expression induced by a dysfunctional miRNA-based regulatory system is thus expected to lead to the development of serious, if not lethal, genetic diseases. A relationship among miRNAs, Dicer, and cancer has recently been suggested. Further investigations will help establish specific causal links between dysfunctional miRNAs and diseases. miRNAs of foreign origin, e.g., viruses, may also be used as specific markers of viral infections. In these cases, miRNA expression profiles could represent a powerful diagnostic tool. Regulatory RNAs may also have therapeutic applications, by which disease-causing genes or viral miRNAs could be neutralized, or functional miRNAs be restored. Will bedside miRNA expression profiling eventually assist physicians in providing patients with accurate diagnosis, personalized therapy, and treatment outcome?
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".