{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T01:02:03Z","timestamp":1773795723876,"version":"3.50.1"},"reference-count":56,"publisher":"Oxford University Press (OUP)","issue":"6","license":[{"start":{"date-parts":[[2025,5,6]],"date-time":"2025-05-06T00:00:00Z","timestamp":1746489600000},"content-version":"vor","delay-in-days":0,"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":["# 22350710182"],"award-info":[{"award-number":["# 22350710182"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,6,2]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>The intricate network of RNA\u2013RNA interactions, crucial for orchestrating essential cellular processes like transcriptional and translational regulations, has been unveiling through high-throughput techniques and computational predictions. As experimental determination of RNA\u2013RNA interactions at the base-pair resolution remains challenging, a timely update for assessing complementary computational tools is necessary, particularly given the recent emergence of deep-learning-based methods.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>Here, we employed base pairs derived from three-dimensional RNA complex structures as a gold standard benchmark to assess the performance of 23 different methods ranging from alignment-based methods, free-energy-based minimization to deep-learning techniques. The result indicates that a deep-learning-based method, SPOT-RNA, can be generalized to make accurate zero-shot predictions of RNA\u2013RNA interactions not only between previously unseen RNA structures but also between RNAs without monomeric structures. The finding underscores the potential of deep learning as a robust tool for advancing our understanding of these complex molecular interactions.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>All data and codes are available at https:\/\/github.com\/meilanglang\/RNA-RNA-Interaction<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btaf289","type":"journal-article","created":{"date-parts":[[2025,5,6]],"date-time":"2025-05-06T16:25:04Z","timestamp":1746548704000},"source":"Crossref","is-referenced-by-count":6,"title":["Benchmarking the methods for predicting base pairs in RNA\u2013RNA interactions"],"prefix":"10.1093","volume":"41","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-9277-2555","authenticated-orcid":false,"given":"Mei","family":"Lang","sequence":"first","affiliation":[{"name":"Institute of Systems and Physical Biology, Shenzhen Bay Laboratory , Shenzhen 518107,","place":["China"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4863-3865","authenticated-orcid":false,"given":"Thomas","family":"Litfin","sequence":"additional","affiliation":[{"name":"Institute for Biomedicine and Glycomics, Griffith University , Southport, QLD 4222,","place":["Australia"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0433-5580","authenticated-orcid":false,"given":"Ke","family":"Chen","sequence":"additional","affiliation":[{"name":"Institute of Systems and Physical Biology, Shenzhen Bay Laboratory , Shenzhen 518107,","place":["China"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0856-2385","authenticated-orcid":false,"given":"Jian","family":"Zhan","sequence":"additional","affiliation":[{"name":"Institute of Systems and Physical Biology, Shenzhen Bay Laboratory , Shenzhen 518107,","place":["China"]},{"name":"Ribopeutic Inc , Futian , Shenzhen, Guangdong 518000,","place":["China"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9958-5699","authenticated-orcid":false,"given":"Yaoqi","family":"Zhou","sequence":"additional","affiliation":[{"name":"Institute of Systems and Physical Biology, Shenzhen Bay Laboratory , Shenzhen 518107,","place":["China"]},{"name":"Institute for Biomedicine and Glycomics, Griffith University , Southport, QLD 4222,","place":["Australia"]}]}],"member":"286","published-online":{"date-parts":[[2025,5,6]]},"reference":[{"key":"2025070408321863800_btaf289-B1","doi-asserted-by":"crossref","first-page":"987","DOI":"10.1016\/j.jmb.2004.10.082","article-title":"Secondary structure prediction of interacting RNA molecules","volume":"345","author":"Andronescu","year":"2005","journal-title":"J Mol Biol"},{"key":"2025070408321863800_btaf289-B2","doi-asserted-by":"crossref","first-page":"551","DOI":"10.1093\/bib\/bby032","article-title":"Prediction of lncRNAs and their interactions with nucleic acids: benchmarking bioinformatics 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