Using matching substructures as an optimization objective for RNA design
Bibliographic record
Abstract
RNA design is a problem that has been shown to be NP-Hard. It is best described as the inverse of RNA folding. RNA folding describes the process of calculating the most likely secondary structure that a strand of nucleotides will fold into. Inversely, RNA design describes the process of designing a strand of nucleotides that will fold into a given secondary structure. The problem is made more difficult by the presence of a second objective, structural stability. Free energy is a measure of structural stability. In previous research, we have attempted to solve this problem using SIMARD (Simulated Annealing RNA Design). SIMARD employs a simulated annealing framework alongside a preselection strategy to design high-quality sequences in a reasonable amount of time. In this paper, we introduce the integration of BEAR (Brand nEw Alphabet for RNAs) to SIMARD as a way of notating secondary structures for quality evaluation. We attempt to design sequences with four different experimental configurations across two data sets. We find that representing our sequences with the BEAR grammar allows us to improve the average structural similarity of our generated sequences. We also find that SIMARD outperforms six other algorithms when running on the Eterna100 benchmark in terms of successfully designed structures.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".