Comparison of Two Folding Functions for RNA Secondary Structure Design
Bibliographic record
Abstract
Using computational programs to predict Ribonucleic acid (RNA) sequence with desired properties has been extensively developed in the past few years. The RNA inverse folding is an NP-Hard problem that describes the process of finding an RNA primary structure that will fold into a given RNA secondary structure. With new advances in the new fields of synthetic biology and RNA nanostructures, there is a growing interest in studying RNA inverse folding. In most of the RNA inverse folding algorithms, in order to evaluate the fitness of a given primary structure, a computationally expensive folding operation is required. In this paper, we incorporated two different folding algorithms in our Simulated Annealing RNA Design (SIMARD) algorithm while employing the Dynamic Exploration Strategy (DES). We employed ViennaRNA and pKiss as our folding operations. Our findings show a significant potential to use both folding operations for our RNA design algorithm. We found that using ViennaRNA tended to produce a more accurate structure, and pKiss tended to produce structures with lower thermodynamic free energy.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".