SARNA-Predict: Using adaptive annealing schedule and inversion mutation operator for RNA secondary structure prediction
Bibliographic record
Abstract
Ribonucleic Acid (RNA) plays a crucial role in many cellular functions including the synthesis of proteins. The structure of RNA is essential for it to serve its purposes within the cell. SARNA-Predict, which has previously been implemented using Simulated Annealing (SA), has shown excellent results predicting the secondary structure of RNA molecules. SA is effective in solving many different optimization problems and for being able to approximate global minima in a solution space. SARNA-Predict uses permutation based SA to heuristically search for RNA secondary structures with close to the minimum free energy with given constraints. A key step in the annealing process is the mutation of the predicted secondary structure in order to search for other potentially lower energy structures. The mutation changes the structure so as to avoid a local minimum and subsequently the free energy of the new structure is evaluated. The purpose of this paper is to evaluate the new inversion mutation operator and compare its use in terms of prediction accuracy to the percentage swap mutation operator previously used in SARNA-Predict. Different annealing schedules used in the SA process are also compared to find the optimal annealing schedule to use for each mutation operator.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".