Protein secondary structure prediction using support vector machines and a codon encoding scheme
Bibliographic record
Abstract
In this study, we evaluate the performance of a protein secondary structure prediction model using a new amino acid "codon" encoding inspired by genetic codon mappings. The dimensionality of the binary codon encoding is less than that of an orthogonal encoding which requires less computations. Protein secondary structure prediction is an important step for machine learning techniques ultimately applied for protein 3D structure prediction. In the proposed model, one-stage binary support vector machines are employed, and the efficiency of the codon encoding to that of a commonly used orthogonal encoding are compared without incorporating protein evolutionary and structural information for an unbiased comparison. The performance of the classification model is measured according to Q3and segment overlap (SOV) scores. The scores are compared with those of the prediction methods using an orthogonal encoding and protein sequence profiles. The experimental results indicate higher prediction accuracy based on Q3SOV scores when sequence profiles are not used. Also, the relative classification scores of the proposed method are comparable with the methods incorporating protein global and evolutionary information. The experimental result implies the encoding scheme is able to integrate the evolutionary information into the prediction model since the encoding is based on genetic codon mappings which are the building blocks of amino acid formations at the primary level of biological processes. The codon encoding is worthwhile to be investigated using more complex learning architectures with the profiles and structural properties of proteins.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".