Thermodynamically based DNA strand design
Bibliographic record
Abstract
We describe a new algorithm for design of strand sets, for use in DNA computations or universal microarrays. Our algorithm can design sets that satisfy any of several thermodynamic and combinatorial constraints, which aim to maximize desired hybridizations between strands and their complements, while minimizing undesired cross-hybridizations. To heuristically search for good strand sets, our algorithm uses a conflict-driven stochastic local search approach, which is known to be effective in solving comparable search problems. The PairFold program of Andronescu et al. [M. Andronescu, Z. C. Zhang and A. Condon (2005) J. Mol. Biol., 345, 987-1001; M. Andronescu, R. Aguirre-Hernandez, A. Condon, and H. Hoos (2003) Nucleic Acids Res., 31, 3416-3422.] is used to calculate the minimum free energy of hybridization between two mismatched strands. We describe new thermodynamic measures of the quality of strand sets. With respect to these measures of quality, our algorithm consistently finds, within reasonable time, sets that are significantly better than previously published sets in the literature.
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How this classification was reachedexpand
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 | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".