Protein Secondary Structure Prediction Using Convolutional Bidirectional GRU
Bibliographic record
Abstract
In this paper, a protein secondary structure prediction method based on convolutional bidirectional GRU Model (CBi-GRU model) is adopted, which combines the advantages of sliding window in extracting local features of data. The use of CNN and Bi-GRU in the construction of the model improves the feature expression and data utilization, and improves the performance of the model. Protein data from FoxChase Institute were used, and high quality, complete and representative CullPDB dataset, CB513, CASP10 and CASP11 datasets were selected to train, test and validate the model. The results show that the proposed method achieves good prediction performance on CASP10 and CASP11 datasets, and the prediction accuracy of Q8 is 76.2% and 76.4%, respectively. Compared with RaptorX-SS, DeepCNF, CGAN-PSSP and other methods, the Q8 evaluation indicators are improved. Compared with the latest research data, our Q8 prediction accuracy is improved by 2% and 5.1%, which shows the effectiveness and superiority of the proposed model.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".