SARNA-Predict: Accuracy Improvement of RNA Secondary Structure Prediction Using Permutation-Based Simulated Annealing
Why this work is in the frame
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Bibliographic record
Abstract
Ribonucleic acid (RNA), a single-stranded linear molecule, is essential to all biological systems. Different regions of the same RNA strand will fold together via base pair interactions to make intricate secondary and tertiary structures that guide crucial homeostatic processes in living organisms. Since the structure of RNA molecules is the key to their function, algorithms for the prediction of RNA structure are of great value. In this article, we demonstrate the usefulness of SARNA-Predict, an RNA secondary structure prediction algorithm based on Simulated Annealing (SA). A performance evaluation of SARNA-Predict in terms of prediction accuracy is made via comparison with eight state-of-the-art RNA prediction algorithms: mfold, Pseudoknot (pknotsRE), NUPACK, pknotsRG-mfe, Sfold, HotKnots, ILM, and STAR. These algorithms are from three different classes: heuristic, dynamic programming, and statistical sampling techniques. An evaluation for the performance of SARNA-Predict in terms of prediction accuracy was verified with native structures. Experiments on 33 individual known structures from eleven RNA classes (tRNA, viral RNA, antigenomic HDV, telomerase RNA, tmRNA, rRNA, RNaseP, 5S rRNA, Group I intron 23S rRNA, Group I intron 16S rRNA, and 16S rRNA) were performed. The results presented in this paper demonstrate that SARNA-Predict can out-perform other state-of-the-art algorithms in terms of prediction accuracy. Furthermore, there is substantial improvement of prediction accuracy by incorporating a more sophisticated thermodynamic model (efn2).
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it