Protein secondary structure prediction using an evolutionary computation method and clustering
Bibliographic record
Abstract
In this paper, we evaluated the performance of an evolutionary-based protein secondary structure (PSS) prediction model which uses the information of amino acid sequences extracted by a clustering technique. The dimension of the classifier's inputs is reduced using a k-means clustering method on sequence segments. The proposed PSS classifier is based on a Genetic Programming (GP) approach that uses IF rules for a multi-target classifier. The GP classifier is evaluated by using protein sequences and the sequence information obtained from the k-means clustering. The GP prediction model's performance is compared with those of feed-forward artificial neural networks (ANNs) and support vector machines (SVMs). The prediction methods are examined with two protein datasets RS126 and CB513. The performance of the three classification models are measured according to Q3and segment overlap (SOV) scores. The prediction models which use clustered data result in average 2% higher prediction accuracy than those using sequence data. In addition, the experimental results indicate the GP model's prediction scores are in average 3% higher than those of the ANN and SVMs models when amino acid sequences or clustered information are explored.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".