Automated, customizable and efficient identification of RNA 3D base pair modules from sequence
Bibliographic record
Abstract
RNA structures possess multiple levels of structural organization.A secondary structure, made of Watson-Crick helices connected by loops, forms a scaffold for the tertiary structure.The 3D structures adopted by these loops are therefore critical determinants shaping the global 3D architecture.Earlier studies showed that these local 3D structures can be described as conserved sets of ordered non-Watson-Crick base pairs called RNA structural modules.Unfortunately, the computational efficiency and scope of the current 3D module prediction methods are too limited to benefit from all the knowledge accumulated in the module databases.We present BayesPairing, an automated, efficient and customizable tool for (i) building Bayesian networks representing RNA 3D modules, and (ii) rapid de novo predictions of 3D modules in sequences.BayesPairing uses a flexible definition of RNA 3D modules which allows it to predict a broader range of modules than current alternatives such as multibranched loops, and features running time improvements that open the door to a broad range of large-scale applications.i
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".