{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T04:07:41Z","timestamp":1783483661767,"version":"3.55.0"},"reference-count":85,"publisher":"Oxford University Press (OUP)","issue":"5","license":[{"start":{"date-parts":[[2023,8,28]],"date-time":"2023-08-28T00:00:00Z","timestamp":1693180800000},"content-version":"vor","delay-in-days":2,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100009318","name":"Helmholtz Association","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100009318","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Munich School for Data Science"},{"DOI":"10.13039\/501100001659","name":"Deutsche Forschungsgemeinschaft","doi-asserted-by":"publisher","award":["SFB\/TR501 84"],"award-info":[{"award-number":["SFB\/TR501 84"]}],"id":[{"id":"10.13039\/501100001659","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,9,20]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>RNA-binding proteins (RBPs) are central actors of RNA post-transcriptional regulation. Experiments to profile-binding sites of RBPs in vivo are limited to transcripts expressed in the experimental cell type, creating the need for computational methods to infer missing binding information. While numerous machine-learning based methods have been developed for this task, their use of heterogeneous training and evaluation datasets across different sets of RBPs and CLIP-seq protocols makes a direct comparison of their performance difficult. Here, we compile a set of 37 machine learning (primarily deep learning) methods for in vivo RBP\u2013RNA interaction prediction and systematically benchmark a subset of 11 representative methods across hundreds of CLIP-seq datasets and RBPs. Using homogenized sample pre-processing and two negative-class sample generation strategies, we evaluate methods in terms of predictive performance and assess the impact of neural network architectures and input modalities on model performance. We believe that this study will not only enable researchers to choose the optimal prediction method for their tasks at hand, but also aid method developers in developing novel, high-performing methods by introducing a standardized framework for their evaluation.<\/jats:p>","DOI":"10.1093\/bib\/bbad307","type":"journal-article","created":{"date-parts":[[2023,8,7]],"date-time":"2023-08-07T08:05:59Z","timestamp":1691395559000},"source":"Crossref","is-referenced-by-count":28,"title":["A systematic benchmark of machine learning methods for protein\u2013RNA interaction prediction"],"prefix":"10.1093","volume":"24","author":[{"given":"Marc","family":"Horlacher","sequence":"first","affiliation":[{"name":"Computational Health Center , Helmholtz Center Munich , Germany"},{"name":"School of Computation, Information and Technology, Technical University Munich (TUM) , Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Giulia","family":"Cantini","sequence":"additional","affiliation":[{"name":"Computational Health Center , Helmholtz Center Munich , Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Julian","family":"Hesse","sequence":"additional","affiliation":[{"name":"Computational Health Center , Helmholtz Center Munich , Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Patrick","family":"Schinke","sequence":"additional","affiliation":[{"name":"Computational Health Center , Helmholtz Center Munich , Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nicolas","family":"Goedert","sequence":"additional","affiliation":[{"name":"Computational Health Center , Helmholtz Center Munich , Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shubhankar","family":"Londhe","sequence":"additional","affiliation":[{"name":"Computational Health Center , Helmholtz Center Munich , Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lambert","family":"Moyon","sequence":"additional","affiliation":[{"name":"Computational Health Center , Helmholtz Center Munich , Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Annalisa","family":"Marsico","sequence":"additional","affiliation":[{"name":"Computational Health Center , Helmholtz Center Munich , Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2023,8,26]]},"reference":[{"key":"2023092216564554300_ref1","first-page":"829","article-title":"A census of human RNA-binding proteins","author":"Gerstberger"},{"issue":"6","key":"2023092216564554300_ref2","doi-asserted-by":"crossref","first-page":"508","DOI":"10.1038\/nmeth.3810","article-title":"Robust transcriptome-wide discovery of RNA-binding protein binding sites with enhanced clip (eclip)","volume":"13","author":"Van Nostrand","year":"2016","journal-title":"Nat Methods"},{"key":"2023092216564554300_ref3","first-page":"185","article-title":"RNA-binding proteins in human genetic disease","author":"Gebauer"},{"issue":"3","key":"2023092216564554300_ref4","doi-asserted-by":"crossref","first-page":"354","DOI":"10.1016\/j.molcel.2018.01.005","article-title":"Advances in clip technologies for studies of protein-RNA interactions","volume":"69","author":"Lee","year":"2018","journal-title":"Mol Cell"},{"issue":"1358","key":"2023092216564554300_ref5","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1007\/978-1-4939-3067-8_10","article-title":"Par-clip: a method for transcriptome-wide identification of RNA binding protein interaction sites","author":"Danan","year":"2016","journal-title":"Methods Mol Biol"},{"issue":"7","key":"2023092216564554300_ref6","doi-asserted-by":"crossref","first-page":"909","DOI":"10.1038\/nsmb.1838","article-title":"Iclip reveals the function of hnrnp particles in splicing at individual nucleotide resolution","volume":"17","author":"K\u00f6nig","year":"2010","journal-title":"Nat Struct Mol Biol"},{"issue":"7818","key":"2023092216564554300_ref7","doi-asserted-by":"crossref","first-page":"711","DOI":"10.1038\/s41586-020-2077-3","article-title":"A large-scale binding and functional map of human RNA-binding proteins","volume":"583","author":"Van Nostrand","year":"2020","journal-title":"Nature"},{"key":"2023092216564554300_ref8","doi-asserted-by":"crossref","first-page":"150929","DOI":"10.1109\/ACCESS.2020.3014996","article-title":"A review about RNA\u2013protein-binding sites prediction based on deep learning","volume":"8","author":"Yan","year":"2020","journal-title":"IEEE Access"},{"issue":"6","key":"2023092216564554300_ref9","article-title":"Recent methodology progress of deep learning for RNA\u2013protein interaction prediction. 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