Networking in a global world: Establishing functional connections between neural splicing regulators and their target transcripts
Bibliographic record
Abstract
Recent genome-wide analyses have indicated that almost all primary transcripts from multi-exon human genes undergo alternative pre-mRNA splicing (AS). Given the prevalence of AS and its importance in expanding proteomic complexity, a major challenge that lies ahead is to determine the functional specificity of isoforms in a cellular context. A significant fraction of alternatively spliced transcripts are regulated in a tissue- or cell-type-specific manner, suggesting that these mRNA variants likely function in the generation of cellular diversity. Complementary to these observations, several tissue-specific splicing factors have been identified, and a number of methodological advances have enabled the identification of large repertoires of target transcripts regulated by these proteins. An emerging theme is that tissue-specific splicing factors regulate coherent sets of splice variants in genes known to function in related biological pathways. This review focuses on the recent progress in our understanding of neural-specific splicing factors and their regulatory networks and outlines existing and emerging strategies for uncovering important biological roles for the isoforms that comprise these networks.
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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 | 0.000 |
| 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 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".