Finding the target sites of <scp>RNA</scp>‐binding proteins
Bibliographic record
Abstract
RNA-protein interactions differ from DNA-protein interactions because of the central role of RNA secondary structure. Some RNA-binding domains (RBDs) recognize their target sites mainly by their shape and geometry and others are sequence-specific but are sensitive to secondary structure context. A number of small- and large-scale experimental approaches have been developed to measure RNAs associated in vitro and in vivo with RNA-binding proteins (RBPs). Generalizing outside of the experimental conditions tested by these assays requires computational motif finding. Often RBP motif finding is done by adapting DNA motif finding methods; but modeling secondary structure context leads to better recovery of RBP-binding preferences. Genome-wide assessment of mRNA secondary structure has recently become possible, but these data must be combined with computational predictions of secondary structure before they add value in predicting in vivo binding. There are two main approaches to incorporating structural information into motif models: supplementing primary sequence motif models with preferred secondary structure contexts (e.g., MEMERIS and RNAcontext) and directly modeling secondary structure recognized by the RBP using stochastic context-free grammars (e.g., CMfinder and RNApromo). The former better reconstruct known binding preferences for sequence-specific RBPs but are not suitable for modeling RBPs that recognize shape and geometry of RNAs. Future work in RBP motif finding should incorporate interactions between multiple RBDs and multiple RBPs in binding to RNA.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".