Multiobjective artificial fish swarm algorithm for multiple sequence alignment
Why this work is in the frame
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Bibliographic record
Abstract
Multiple sequence alignment (MSA) represents a basic task for many bioinformatics applications. MSA allows finding common conserved regions among various sequences of proteins or DNA. However, to find the optimal multiple sequence alignment, it is necessary to design an efficient exploration approach that could explore a huge number of possible multiple sequence alignments. As well as, it is required to use a powerful evaluation method to assess the biological relevance of these multiple sequence alignment. To address these main problems, this article presents a multiobjective artificial fish swarm algorithm (MOAFS) to solve multiple sequence alignment. MOAFS uses the behaviors of artificial fish swarm algorithm such as the cooperation, decentralization and parallelism to ensure a good trade-off between the exploration and the exploitation of the search space of MSA problem. To preserve the quality and consistency of alignment, two fitness functions have been simultaneously used by the MOAFS algorithm: (i) Weighted Sum of Pairs to determine similar regions horizontally and (ii) Similarity function to determine vertically similar regions between the sequences of an alignment. Following the exploration of space search, the Pareto-optimal set is obtained by MOAFS which performs the optimal multiple sequence alignments for both fitness functions. The performance of MOAFS algorithm has been proved by comparing our algorithm with different progressive alignment methods, and other alignment methods based on evolutionary algorithms with single-objective and many-objective. The experiment results conducted on BAliBASE 2.0 and BAliBASE 3.0 benchmark confirm that the MOAFS algorithm provides a greater accuracy statistical significance in terms of SP or CS scores.
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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 | 0.000 | 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