Bibliographic record
Abstract
Typically, cancer is thought to arise due to DNA mutations, dysregulated transcription and/or aberrant signalling. Recently, it has become clear that dysregulated mRNA processing, mRNA export and translation also contribute to malignancy. RNA processing events result in major modifications to the physical nature of mRNAs such as the addition of the methyl-7-guanosine cap, the removal of introns and the addition of polyA tails. mRNA processing is a critical determinant for the protein-coding capacity of mRNAs since these physical changes impact the efficiency by which a given transcript can be exported to the cytoplasm and translated into protein. While many of these mRNA metabolism steps were considered constitutive housekeeping activities, they are now known to be highly regulated with combinatorial and multiplicative impacts i.e. one event will influence the capacity to undergo others. Furthermore, alternative splicing and/or cleavage and polyadenylation can produce transcripts with alternative messages and new functionalities. The coordinated processing of groups of functionally related RNAs can potently re-wire signalling pathways, modulate survival pathways and even re-structure the cell. As postulated by the RNA regulon model, combinatorial regulation of these groups is achieved by the presence of shared cis-acting elements (known as USER codes) which recruit machinery for processing, export or translation. In all, dysregulated RNA metabolism in cancer gives rise to an altered proteome that in turn elicits biological responses related to malignancy. Studies of these events in cancer revealed new mechanisms underpinning malignancies and unearthed novel therapeutic opportunities. In all, cancer cells coopt RNA processing, export and translation to support their oncogenic activity.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.007 |
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".