Consistent Consideration of RNA Structural Alignments Improves Prediction Accuracy of RNA Secondary Structures
Bibliographic record
Abstract
ABSTRACT The probabilistic consideration of the global pairwise sequence alignment of two RNAs tied with their global single secondary structures, or global pairwise structural alignment, is known to predict more accurately global single secondary structures of unaligned homologs by discriminating between conserved local single secondary structures and those not conserved. However, conducting rigorously this consideration is computationally impractical and thus has been done to decompose global pairwise structural alignments into their independent components, i.e. global pairwise sequence alignments and single secondary structures, by conventional methods. ConsHomfold and ConsAlifold, which predict the global single and consensus secondary structures of unaligned and aligned homologs considering consistently preferable (or sparse) global pairwise structural alignments on probability respectively, were developed and implemented in this study. These methods demonstrate the best trade-off of prediction accuracy while exhibiting comparable running time compared to conventional methods. ConsHomfold and ConsAlifold optionally report novel types of loop accessibility, which are useful for the analysis of sequences and secondary structures. These accessibilities are average on sparse global pairwise structural alignment and can be computed to extend the novel inside-outside algorithm proposed in this study that computes pair alignment probabilities on this alignment.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 0.000 |
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".