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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".