Long non‐coding RNAs in development and disease: conservation to mechanisms
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.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".