Predicting Enhancer-Promoter Interactions Using a Stacking-Based Ensemble Strategy

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ID: 316050
2026
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Abstract
MOTIVATION: Enhancer-promoter interactions (EPIs) are essential for gene regulation and disease progression. Recent studies have shown that distal enhancers can regulate target genes through interactions with nearby promoters, providing important insights into transcriptional regulation mechanisms. Although high-throughput experimental techniques have enabled large-scale identification of EPIs, these methods are often costly and time-consuming. In addition, existing computational approaches still face challenges in effectively integrating heterogeneous feature representations from different cell lines. RESULTS: We propose a stacked ensemble framework for EPI prediction that integrates feature representations from diverse cell line datasets using multiple machine learning algorithms. The extracted complementary patterns are further combined by an XGBoost classifier to improve robustness against overfitting. Experiments on six independent datasets show that the proposed method achieves superior accuracy and generalization compared with existing EPI prediction models, with an average AUROC of 0.909 while maintaining computational efficiency. AVAILABILITY: The source code and its archived release are available at GitHub and Zenodo. The Zenodo archive provides a versioned snapshot of the repository: https://zenodo.org/records/19952998. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Reference Key
openalex_W7163673973 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Zhichao Xiao, Haibo Ji, Quan Zou, Yijie Ding, Liang Yu
Journal BMC Bioinformatics
Year 2026
DOI
10.1093/bioinformatics/btag359
URL
https://doi.org/10.1093/bioinformatics/btag359
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