A novel stacking-based classifier for identifying antifreeze protein using latent semantic analysis
Bibliographic record
Abstract
Antifreeze proteins (AFPs) are key in combating cold in living organisms and preventing ice morphogenesis. These proteins have applications in cryopreservation, food preservation, and biotechnology. Factors such as accurate prediction of AFP are considered essential for advancing these fields. In this study, a novel method, StackAFP, for predicting antifreeze proteins has been developed using the stacking method and latent semantic analysis (LSA) as the feature extraction technique. Four machine learning algorithms, such as random forest (RF), XGboost (XGB), CatBoost (CAT), and LightGBM (LGBM), were used as the baseline models, and LGBM is employed as the metaclassifier to develop StackAFP. StackAFP is compared with different conventional machine learning to ensure the robustness of the proposed method. StackAFP shows potentiality with an accuracy of 0.9997, a Matthews Correlation Coefficient (MCC), and a Kappa value of 0.9944. StackAFP outperformed the entire applied conventional machine learning model. Finally, it was found that StackAFP also outperformed the existing methods of identifying AFPs. The performance of StackAFP demonstrates its effectiveness, highlights its potential in bioinformatics, and advances our knowledge of AFPs. The main limiting factor is the severe class imbalance in the dataset, which might influence the model generalization. In future studies, we will employ contrastive learning for better feature representation learning and generalization and reliability of the AFP prediction 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".