StackAPP: Advancing autophagy protein identification with ensemble learning
Bibliographic record
Abstract
Autophagy is an important cell process that may be critical for various physiological activities as well as maintenance of the cellular bioenergetic and metabolic homeostasis. Identifying the proteins involved in autophagy is essential for understanding autophagy pathways and developing treatments for autophagy-related disorders. This work introduces an innovative approach to the prediction of autophagy proteins that involves the integration of stacking classifiers with the feature fusion of Amphiphilic Pseudo Amino Acid Composition and Amino Acid Composition. Initially, protein sequences are used to extract Amphiphilic Pseudo Amino Acid Composition and Amino Acid Composition features. The complementary data collected by Amphiphilic Pseudo Amino Acid Composition and Amino Acid Composition are then integrated using a feature fusion technique. Stacking classifiers combines multiple base classifiers to improve predictive performance, using the fused features as input. The proposed method proves its efficacy in the identification of autophagy proteins by achieving an impressive accuracy of 0.9606 and the Matthews correlation coefficient (MCC) of 0.9241 on the independent test. Further, our methodology is better than the standard methods in terms of predictive accuracy, as evidenced through comparative analysis. Overall, the current study provides a realistic model for the prediction of autophagy proteins with prospects for use in the protein prediction field as well as the field of bioinformatics and biomedical to enhance future research directions.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".