Networking in a global world: Establishing functional connections between neural splicing regulators and their target transcripts
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
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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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 | 0.000 | 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