Long non‐coding RNAs in development and disease: conservation to mechanisms
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Our genomes contain the blueprint of what makes us human and many indications as to why we develop disease. Until the last 10 years, most studies had focussed on protein-coding genes, more specifically DNA sequences coding for proteins. However, this represents less than 5% of our genomes. The other 95% is referred to as the 'dark matter' of our genomes, our understanding of which is extremely limited. Part of this 'dark matter' includes regions that give rise to RNAs that do not code for proteins. A subset of these non-coding RNAs are long non-coding RNAs (lncRNAs), which in particular are beginning to be dissected and their importance to human health revealed. To improve our understanding and treatment of disease it is vital that we understand the molecular and cellular function of lncRNAs, and how their misregulation can contribute to disease. It is not yet clear what proportion of lncRNAs is actually functional; conservation during evolution is being used to understand the biological importance of lncRNA. Here, we present key themes within the field of lncRNAs, emphasising the importance of their roles in both the nucleus and the cytoplasm of cells, as well as patterns in their modes of action. We discuss their potential functions in development and disease using examples where we have the greatest understanding. Finally, we emphasise why lncRNAs can serve as biomarkers and discuss their emerging potential for therapy. © 2020 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of Pathological Society of Great Britain and Ireland.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| 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 it