A technique of genetic algorithm and sequence synthesis for multiple molecular sequence alignment
Why this work is in the frame
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Bibliographic record
Abstract
The currently used techniques for multiple sequence alignment are characterized by great computational complexity, which prevents the techniques from wider use. The research reported in the paper is aimed at developing a new technique for efficient multiple sequence alignment. The new technique consists of a genetic algorithm and a sequence synthesis method. The genetic algorithm identifies matches and the sequence synthesis method handles mismatches. Genetic algorithms are stochastic approaches for efficient and robust search. By converting biomolecular sequence alignment into a problem of searching for near-optimal points in a "pre-alignment space", a genetic algorithm can be used to find good alignments very efficiently. Experiments on real data sets have shown that the average computing time of this technique may be two or three orders lower than an technique based on pairwise dynamic programming, while the alignment qualities are very similar.
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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