StackHPpred: A Stacking-based ensemble learning framework for the identification of peptide hormones using multi-view feature representations
- 1University of South Dakota
Abstract
Peptide hormones are important signaling molecules that regulate diverse physiological processes and have substantial therapeutic relevance. However, their experimental identification can be challenging because of low abundance, limited stability, and complex post-translational processing, highlighting the need for reliable computational approaches. Here, we present StackHPpred, a stacking-based ensemble framework for accurate identification of hormone peptides (HPs) from amino acid sequences. We systematically evaluated 56 feature representations, comprising 35 conventional sequence descriptors and 21 PLM/NLP-based embeddings, across 10 machine-learning classifiers, generating 560 baseline models. Prediction-level correlation analysis was subsequently employed to identify 14 complementary, non-redundant feature representations, which were integrated through feature- and classifier-level stacking strategies using out-of-fold predicted probabilities. The final StackHPpred model, based on the top three feature representations CTDD, KSCTriad, and PTAB, achieved an AUC of 0.9965, MCC of 0.9506, ACC of 0.9752, and F1-score of 0.9749 on the independent dataset. StackHPpred was further evaluated under increasingly imbalanced independent datasets with positive-to-negative ratios ranging from 1:10 to 1:50, demonstrating robust predictive performance under challenging screening conditions. t-SNE visualization and SHAP analysis further showed improved class separation and highlighted the contributions of multiple classifier-feature combinations to the final prediction. Overall, StackHPpred provides a robust and interpretable framework for distinguishing HPs from non-HPs and is freely available as a web server at https://jayasreekirthipati.org/StackHPpred/, with a standalone implementation available at https://github.com/JayasreeKirthipati04/StackHPpred.
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BibTeX
@misc{kirthipati2026stackhppred,
title = {{StackHPpred: A Stacking-based ensemble learning framework for the identification of peptide hormones using multi-view feature representations}},
author = {J. Kirthipati and M. K. Chemarthi Ravi and M. Kirthipati},
howpublished = {bioRxiv},
year = {2026},
month = sep,
doi = {10.64898/2026.09.16.751134},
url = {https://www.biorxiv.org/content/10.64898/2026.09.16.751134v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - StackHPpred: A Stacking-based ensemble learning framework for the identification of peptide hormones using multi-view feature representations
AU - J. Kirthipati
AU - M. K. Chemarthi Ravi
AU - M. Kirthipati
PY - 2026
DA - 2026/09/16
PB - bioRxiv
DO - 10.64898/2026.09.16.751134
UR - https://www.biorxiv.org/content/10.64898/2026.09.16.751134v1
AB - Peptide hormones are important signaling molecules that regulate diverse physiological processes and have substantial therapeutic relevance. However, their experimental identification can be challenging because of low abundance, limited stability, and complex post-translational processing, highlighting the need for reliable computational approaches. Here, we present StackHPpred, a stacking-based ensemble framework for accurate identification of hormone peptides (HPs) from amino acid sequences. We systematically evaluated 56 feature representations, comprising 35 conventional sequence descriptors and 21 PLM/NLP-based embeddings, across 10 machine-learning classifiers, generating 560 baseline models. Prediction-level correlation analysis was subsequently employed to identify 14 complementary, non-redundant feature representations, which were integrated through feature- and classifier-level stacking strategies using out-of-fold predicted probabilities. The final StackHPpred model, based on the top three feature representations CTDD, KSCTriad, and PTAB, achieved an AUC of 0.9965, MCC of 0.9506, ACC of 0.9752, and F1-score of 0.9749 on the independent dataset. StackHPpred was further evaluated under increasingly imbalanced independent datasets with positive-to-negative ratios ranging from 1:10 to 1:50, demonstrating robust predictive performance under challenging screening conditions. t-SNE visualization and SHAP analysis further showed improved class separation and highlighted the contributions of multiple classifier-feature combinations to the final prediction. Overall, StackHPpred provides a robust and interpretable framework for distinguishing HPs from non-HPs and is freely available as a web server at https://jayasreekirthipati.org/StackHPpred/, with a standalone implementation available at https://github.com/JayasreeKirthipati04/StackHPpred.
ER -