Simplicity in RNA Secondary Structure Alignment: Towards biologically plausible alignments
Bibliographic record
Abstract
Ribonucleic acid (RNA) molecules contain the genetic information that regulates the functions of organisms. Given two different molecules, a preserved function corresponds to a preserved secondary RNA structure. Hence, RNA secondary-structure comparison is essential in predicting the functions of a newly discovered molecule. In this paper, we discuss our SPRC method for RNA structure comparison. In this work, we developed, a novel tree representation of RNA that reflects both its primary and secondary structure and a tree-alignment algorithm, which, given the tree representations of two RNA molecules, produces a sequence of mutations that could transform one RNA molecule to the other. Our SPRC algorithm extends the Zhang-Shasha tree-edit distance calculation algorithm in two ways: first, in addition to the distance, it reports all editing sequences with the same minimum edit cost, and second, it uses a biologically-inspired affine cost function. Furthermore, the SPRC method proposes set of heuristics designed to filter the produced solution set to recommend the simplest editing sequence, as corresponding to the most biologically correct alignment. Experiments on three 5S rRNA families: archaea, eubacteria, and eukaryota, show that SPRC is very effective in producing biologically meaningful RNA secondary structure alignments
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".