{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,24]],"date-time":"2026-06-24T13:48:46Z","timestamp":1782308926426,"version":"3.54.5"},"reference-count":44,"publisher":"Oxford University Press (OUP)","issue":"6","license":[{"start":{"date-parts":[[2026,6,5]],"date-time":"2026-06-05T00:00:00Z","timestamp":1780617600000},"content-version":"vor","delay-in-days":4,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62472344"],"award-info":[{"award-number":["62472344"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62272065"],"award-info":[{"award-number":["62272065"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Natural Science Basic Research Program of Shaanxi","award":["2025JC-JCQN-094"],"award-info":[{"award-number":["2025JC-JCQN-094"]}]},{"name":"Xidian University Specially Funded Project for Interdisciplinary Exploration","award":["TZJH2024027"],"award-info":[{"award-number":["TZJH2024027"]}]},{"name":"Zhejiang Provincial Natural Science Foundation of China","award":["LY23F020003"],"award-info":[{"award-number":["LY23F020003"]}]},{"name":"Municipal Government of Quzhou","award":["2024D002"],"award-info":[{"award-number":["2024D002"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,6,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Motivation<\/jats:title>\n                    <jats:p>Enhancer\u2013promoter 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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability<\/jats:title>\n                    <jats:p>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<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btag359","type":"journal-article","created":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T11:32:07Z","timestamp":1780399927000},"source":"Crossref","is-referenced-by-count":0,"title":["Predicting enhancer-promoter interactions using a stacking-based ensemble strategy"],"prefix":"10.1093","volume":"42","author":[{"given":"Zhichao","family":"Xiao","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, Xidian University , Xi'an 710126,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haibo","family":"Ji","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Xidian University , Xi'an 710126,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6406-1142","authenticated-orcid":false,"given":"Quan","family":"Zou","sequence":"additional","affiliation":[{"name":"Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China , Quzhou 324000,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3438-9259","authenticated-orcid":false,"given":"Yijie","family":"Ding","sequence":"additional","affiliation":[{"name":"Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China , Quzhou 324000,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8351-3332","authenticated-orcid":false,"given":"Liang","family":"Yu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Xidian University , Xi'an 710126,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2026,6,4]]},"reference":[{"key":"2026062409114255500_btag359-B1","volume-title":"UCI Machine Learning Repository","author":"Asuncion","year":"2007"},{"key":"2026062409114255500_btag359-B2","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach Learn"},{"key":"2026062409114255500_btag359-B3","doi-asserted-by":"crossref","first-page":"D1261","DOI":"10.1093\/nar\/gkae1022","article-title":"webTWAS 2.0: update platform for identifying complex disease susceptibility genes through transcriptome-wide association study","volume":"53","author":"Cao","year":"2025","journal-title":"Nucleic Acids Res"},{"key":"2026062409114255500_btag359-B4","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13059-021-02453-5","article-title":"Chromatin Interaction Neural Network (ChINN): a machine learning-based method for predicting chromatin interactions from DNA sequences","volume":"22","author":"Cao","year":"2021","journal-title":". 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