Fluorophore‐binding <scp>RNA</scp> aptamers and their applications
Bibliographic record
Abstract
Why image RNA? Of all the biological molecules, RNA exhibits the most diverse range of functions. Evidence suggests that transcription produces a wide range of noncoding RNAs (ncRNAs), both short (e.g., siRNAs, miRNAs) and long (e.g., telomeric RNAs) that regulate many aspects of gene expression, including the epigenetic processes that underlie cell fate determination, polarization, and morphogenesis. All these functions are realized through the exquisite temporal and spatial control of RNA expression levels and the stability of specific RNAs within well-defined sub-cellular compartments. Given the central importance of RNA in dictating cell behavior via gene-related functions, there is a great demand for RNA imaging methods so as to determine the composition of the cellular 'transcriptome' and to acquire a complete spatial-temporal profile of RNA localization. Recent advances in fluorophore-binding RNA aptamers promise to provide exactly this knowledge, which can ultimately advance our understanding of cell function and behavior in conditions of health and disease, and in response to external stimuli. WIREs RNA 2016, 7:843-851. doi: 10.1002/wrna.1383 For further resources related to this article, please visit the WIREs website.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".