Length-Based Substructure Mutation Policies for Improved RNA Design in Simulated Annealing
Bibliographic record
Abstract
RNA Design is a crucial bioinformatics problem to tailor specific RNA sequences into structures that guide our biology and medicine. Because these computer generated sequences are often statistically different from observed RNA sequence and do not fold as intended in real lab conditions, methods are developed to ensure more biological consistency. One approach is substructure restriction where any solutions are composed of observed RNA substructures recorded in a database. However, studies on this restricted problem are limited: while our Applied Research Lab's Simulated Annealing solution (SIMARD) uses substructures it only considers a single mutation policy and single method of generating substructure consistent sequences. We therefore propose two new policies of mutation: uniform, randomly modifying any structure, and length proportional, substructures are swapped randomly in proportion to their RNA length to target substructures that cover the most bases of a problem. In experiments on roughly fifty RNA Design problems, we conclude the potential value of these substructure based mutation methods resulting in solutions potentially hundreds of bases closer to the target folded structure than previous Simulated Annealing solutions.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".