Bibliographic record
Abstract
Alternative splicing is widely considered to be a major mechanism underlying the evolution of increased cellular and functional complexity in vertebrate species and is especially prevalent in the mammalian nervous system. Microarray profiling and, more recently, high‐throughput sequencing has resulted in the identification of a myriad of nervous system‐regulated exons. Many of these are located in widely expressed genes that have critical nervous system‐specific functions. Our research is currently focusing on elucidating the cis ‐acting "code" and corresponding trans‐acting factors responsible for the regulation of these exons, and their specific roles in nervous system formation and function. In collaboration with the group of Brendan Frey (Dept. of Electrical and Computer Engineering, University of Toronto) we have developed a new machine learning algorithm that can predict nervous system and other tissue‐regulated alternative splicing patterns from sequence features alone. Using other genome‐wide strategies as well as focused experimental methods we are identifying and characterizing trans ‐acting factors that link to specific elements of the cis ‐regulatory code. A new trans ‐acting factor emerging from one of the screens is the neural‐specific SR‐related protein of 100 kDa (nSR100). This protein is vertebrate‐lineage‐specific and functions as a coactivator to regulate ~10‐12% of nervous‐system specific exons via C/U‐rich motifs concentrated in flanking intron sequences. Knockdown of this protein disrupts the regulation of a network of alternative exons associated with neuronal differentiation and nervous system development.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".