CONSENSUS CONTACT PREDICTION BY LINEAR PROGRAMMING
Why this work is in the frame
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Bibliographic record
Abstract
Protein inter-residue contacts are of great use for protein structure determination or prediction. Recent CASP events have shown that a few accurately predicted contacts can help improve both computational efficiency and prediction accuracy of the ab inito folding methods. This paper develops an integer linear programming (ILP) method for consensus-based contact prediction. In contrast to the simple "majority voting" method assuming that all the individual servers are equal and independent, our method evaluates their correlations using the maximum likelihood method and constructs some latent independent servers using the principal component analysis technique. Then, we use an integer linear programming model to assign weights to these latent servers in order to maximize the deviation between the correct contacts and incorrect ones; our consensus prediction server is the weighted combination of these latent servers. In addition to the consensus information, our method also uses server-independent correlated mutation (CM) as one of the prediction features. Experimental results demonstrate that our contact prediction server performs better than the "majority voting" method. The accuracy of our method for the top L/5 contacts on CASP7 targets is 73.41%, which is much higher than previously reported studies. On the 16 free modeling (FM) targets, our method achieves an accuracy of 37.21%.
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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