StackTHP: A stacking ensemble model for accurate prediction of tumor-homing peptides in cancer therapy
Bibliographic record
Abstract
The tumor-homing peptides (THPs) have emerged as one of the attractive resources for targeted cancer therapy, being able to bind and penetrate tumor cells selectively while ignoring adjacent healthy tissues. Therefore, the computational models to predict THPs became popular very rapidly, since laboratory methods are slow and resourceful. Herein, we are proposing StackTHP, a newly developed stacking-ensemble model aimed at further improving THP prediction accuracy. StackTHP implements multiple feature extraction methods, including amino acid composition (AAC), and pseudo amino acid composition (PAAC) together with classical machine learning classifiers like Extra Trees, Random Forest, and AdaBoost, while the logistic regression-based meta-classifier is used for the stacking framework. StackTHP outperformed all other models, producing an accuracy of 91.92 %, Matthew's correlation coefficient (MCC) of 0.8415, AUC of 0.977 on benchmark datasets, indicates that it is better than approaches attempted earlier and provides a robust solution for proceeding towards the discovery and development of peptide-based cancer therapies. Future research will focus on the application of StackTHP over more diverse sets of data along with some hybrid methods to enhance the prediction capability. The dataset and the code are available at the following link: https://github.com/Ashikur562/StackTHP.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".