A study of different annealing schedules in SARNA-predict
Bibliographic record
Abstract
Purpose – The purpose of this paper is to present a study of the effect of different types of annealing schedules for a ribonucleic acid (RNA) secondary structure prediction algorithm based on simulated annealing (SA). Design/methodology/approach – An RNA folding algorithm was implemented that assembles the final structure from potential substructures (helixes). Structures are encoded as a permutation of helixes. An SA searches this space of permutations. Parameters and annealing schedules were studied and fine-tuned to optimize algorithm performance. Findings – In comparing with mfold, the SA algorithm shows comparable results (in terms ofF-measure) even with a less sophisticated thermodynamic model. In terms of average specificity, the SA algorithm has provided surpassing results. Research limitations/implications – Most of the underlying thermodynamic models are too simplistic and incomplete to accurately model the free energy for larger structures. This is the largest limitation of free energy-based RNA folding algorithms in general. Practical implications – The algorithm offers a different approach that can be used in practice to fold RNA sequences quickly. Originality/value – The algorithm is one of only two SA-based RNA folding algorithms. The authors use a very different encoding, based on permutation of candidate helixes. The in depth study of annealing schedules and other parameters makes the algorithm a strong contender. Another benefit is that new thermodynamic models can be incorporated with relative ease (which is not the case for algorithms based on dynamic programming).
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".