TLS, EWS and TAF15: a model for transcriptional integration of gene expression
Bibliographic record
Abstract
Multifunctional proteins are demonstrating that gene expression is not a series of compartmentalized events beginning with transcription and culminating in delivery of mature mRNA into the cytoplasm, but an integrated pathway of transcription, splicing, RNA metabolism and subcellular targeting of translation. One such multifunctional family is made up of the RNA-binding proteins TLS, EWS and TAF15. These three proteins each contribute a potent transcriptional activation domain to oncogenic fusion proteins, and the formation of these fusion genes are thought to be the primary causes of their associated cancers. Wild-type TLS, EWS and TAF15 can function as classical transcription factors in addition to their better-known functions in splicing and mRNA transport. The interaction between TLS and the stress-response protein YB-1 is an example of how these proteins can induce a multi-faceted change in gene expression, as they can interact to induce changes in both transcription and splicing of target genes. Investigating the multiple functions of TLS, EWS and TAF15 will enhance our understanding of gene expression as a whole, and also allow us to better understand how these proteins may be contributing to the oncogenic pathways the associated fusion proteins initiate.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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".