Improved Prediction of Relative Solvent Accessibility Using Two-stage Support Vector Regression
Bibliographic record
Abstract
Predicted relative solvent accessibility (RSA) provides useful information for prediction of binding sites and reconstruction of the 3D-structure based on a protein sequence, which are at the very core of proteomics. Several RSA prediction methods including those that generate real values and those that predict discrete states (buried vs. exposed) have been published. We propose a novel method for real valued prediction that aims to improve the prediction quality when compared with the existing methods. The proposed method combines Support Vector Regression (SVR) predictors into a two-stage architecture. The improved prediction quality comes from a composite sequence representation, which includes a custom-selected subset of features from the PSTBLAST profile, secondary structure predicted with PSTPRED, and binary code that indicates position of a given residue with respect to sequence termini. Based on empirical evaluation with a standard benchmark dataset, the proposed method obtains the mean absolute error (MAE) equal 0.143, which corresponds to 6% error rate reduction when compared with the best performing competing method that obtains 0.152 MAE on this dataset.
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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 | 0.002 | 0.005 |
| 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".