Protein Sequence Prediction Based on Feature Combination and Attention Mechanism
Bibliographic record
Abstract
Identifying protein functions is crucial for understanding biological mechanisms and advancing life sciences. However, current protein prediction methods fail to fully exploit sequence information, resulting in limitations in prediction accuracy. To address this issue, we propose a protein sequence prediction network (ECPN-HFGF-ATT) that incorporates feature combination and an attention mechanism. The proposed method initially employs a residual network to extract shared features from protein sequences, followed by hierarchical and global feature extraction modules to capture more detailed hierarchical and global sequence characteristics. Next, the prediction results from both feature types are integrated, and the attention mechanism is applied to enhance prediction accuracy. Experimental results demonstrate that the ECPN-HFGF-ATT method achieves superior performance in protein sequence prediction, with macro and micro F1 scores of 95.5% and 99.0%, respectively. The ECPN-HFGF-ATT method effectively integrates hierarchical and global features, enabling rapid and accurate identification of protein sequences. Compared to commonly used methods, this approach significantly enhances prediction accuracy, offering a powerful tool for advancing protein sequence research and applications.
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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 | 0.001 |
| 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 | 0.001 |
| 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".