An <i>exon-centric</i> perspective<sup>1</sup>Canadian Society of Molecular Biosciences (CSMB) Senior Investigator Award
Bibliographic record
Abstract
During the past ten years, remarkable progress has been made in our understanding of the complexity and regulation of alternative splicing. The generation of large datasets of quantitative alternative splicing profiling information has revealed that transcripts from at least 95% of multi-exon human genes undergo alternative splicing, and that thousands of exons in mammalian transcriptomes are subject to striking regulatory patterns. Together with advanced computational methods, these datasets have enabled the inference of a predictive code for tissue-dependent alternative splicing. This code has further provided new insight into splicing regulatory mechanisms. Collectively, these approaches are revealing the existence of discrete networks of exons that are coordinately regulated in diverse biologically normal and disease contexts. A major challenge ahead is to systematically determine the functions of exons comprising these exon networks as well as the factors and mechanisms responsible for their regulation. This perspective provides an account of progress in these areas and also discusses future avenues of exon-centric exploration.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.052 | 0.016 |
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".