{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,4,25]],"date-time":"2025-04-25T04:14:05Z","timestamp":1745554445553,"version":"3.37.3"},"reference-count":43,"publisher":"Oxford University Press (OUP)","issue":"Supplement_1","license":[{"start":{"date-parts":[[2020,7,13]],"date-time":"2020-07-13T00:00:00Z","timestamp":1594598400000},"content-version":"vor","delay-in-days":12,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"name":"Genome Quebec\/Canada"},{"DOI":"10.13039\/501100019217","name":"Institut de Valorisation des Donn\u00e9es","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100019217","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Canada CIFAR AI Chair"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,7,1]]},"abstract":"Abstract<\/jats:title>\n \n Motivation<\/jats:title>\n RNA-protein interactions are key effectors of post-transcriptional regulation. Significant experimental and bioinformatics efforts have been expended on characterizing protein binding mechanisms on the molecular level, and on highlighting the sequence and structural traits of RNA that impact the binding specificity for different proteins. Yet our ability to predict these interactions in silico remains relatively poor.<\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n In this study, we introduce RPI-Net, a graph neural network approach for RNA-protein interaction prediction. RPI-Net learns and exploits a graph representation of RNA molecules, yielding significant performance gains over existing state-of-the-art approaches. We also introduce an approach to rectify an important type of sequence bias caused by the RNase T1 enzyme used in many CLIP-Seq experiments, and we show that correcting this bias is essential in order to learn meaningful predictors and properly evaluate their accuracy. Finally, we provide new approaches to interpret the trained models and extract simple, biologically interpretable representations of the learned sequence and structural motifs.<\/jats:p>\n <\/jats:sec>\n \n Availability and implementation<\/jats:title>\n Source code can be accessed at https:\/\/www.github.com\/HarveyYan\/RNAonGraph.<\/jats:p>\n <\/jats:sec>\n \n Supplementary information<\/jats:title>\n Supplementary data are available at Bioinformatics online.<\/jats:p>\n <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btaa456","type":"journal-article","created":{"date-parts":[[2020,7,1]],"date-time":"2020-07-01T19:11:27Z","timestamp":1593630687000},"page":"i276-i284","source":"Crossref","is-referenced-by-count":42,"title":["Graph neural representational learning of RNA secondary structures for predicting RNA-protein interactions"],"prefix":"10.1093","volume":"36","author":[{"given":"Zichao","family":"Yan","sequence":"first","affiliation":[{"name":"School of Computer Science, McGill University , Montreal, QC H3A 2B2, Canada"},{"name":"MILA, Quebec AI Institute , Montreal, QC H2S 3H1, Canada"}]},{"given":"William L","family":"Hamilton","sequence":"additional","affiliation":[{"name":"School of Computer Science, McGill University , Montreal, QC H3A 2B2, Canada"},{"name":"MILA, Quebec AI Institute , Montreal, QC H2S 3H1, Canada"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9555-860X","authenticated-orcid":false,"given":"Mathieu","family":"Blanchette","sequence":"additional","affiliation":[{"name":"School of Computer Science, McGill University , Montreal, QC H3A 2B2, Canada"}]}],"member":"286","published-online":{"date-parts":[[2020,7,13]]},"reference":[{"key":"2024021913333893700_btaa456-B1","doi-asserted-by":"crossref","first-page":"D180","DOI":"10.1093\/nar\/gkr1007","article-title":"doRiNA: a database of RNA interactions in post-transcriptional regulation","volume":"40","author":"Anders","year":"2012","journal-title":"Nucleic Acids Res"},{"key":"2024021913333893700_btaa456-B2","doi-asserted-by":"crossref","first-page":"168","DOI":"10.1038\/nsmb1053","article-title":"Sequence-specific recognition of RNA hairpins by the SAM domain of Vts1p","volume":"13","author":"Aviv","year":"2006","journal-title":"Nat. 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