An improved multi-start particle swarm-based algorithm for protein structure comparison
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper proposes a novel particle-swarm based approach for protein structure alignment and comparison. Applying heuristic search to discover similar protein substructure patterns can be easily trapped in certain regions of the sparse and challenging problem search space. Diversification, or restarting the heuristic search, is one of the common strategies used to escape local optima. Agile Particle Swarm Optimization (APSO) is a recent multi-start PSO that addresses the question of when to best restart swarm particles. This paper focuses on where and how to restart the swarm. Another challenge of applying a heuristic search to protein structures is that the fitness landscape does not necessarily guide to the optimal region. To address this issue, we propose the Targeted Agile PSO (TA-PSO) that uses a dynamic window-based search for automatic, variable-size pattern discovery in protein structures. The TA-PSO automatically builds a guiding list of potential patterns and uses it during the search process, which helps to find better solutions faster. The proposed TA-PSO showed up to 4 times improved performance that is 3.5 times faster and 6 times more robust/consistent compared with the traditional --non-targeted search
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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 | 0.001 | 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 | 0.001 | 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