STACKION: Ion Channel-Modulating Peptides Identification Using Stacking-Based Ensemble Machine Learning
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Bibliographic record
Abstract
Ion channel-modulating peptides play a crucial role in various physiological processes, making their identification a significant area of research. In this study, we present STACKION, a novel stacking-based ensemble machine-learning approach for the identification of ion channel-modulating peptides. Five feature extraction methods, including amino acid composition (AAC), pseudo-amino acid composition (PAAC), dipeptide composition (DPC), tripeptide composition (TPC), and composition-transition-distribution (CTDC), were employed to extract discriminative features from peptide sequences. Additionally, eight machine learning algorithms were applied to build predictive models. Through extensive experiments and evaluation, we demonstrate that our proposed method, STACKION, consistently outperformed the other feature extraction methods in predicting sodium (Na <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">+</sup> ) and calcium (Ca <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">+</sup> ) ion channel-modulating peptides with the DPC feature extraction method. Moreover, when combined with the PAAC feature extraction method, STACKION demonstrated excellent predictive accuracy in identifying potassium (K <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">+</sup> ) ion channel-modulating peptides. These findings highlight the effectiveness of STACKION in peptide identification, with potential implications for understanding peptide functionality and the development of therapeutic agents targeting ion channels.
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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 | 0.001 | 0.000 |
| 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 it