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To understand the function of lncRNAs, a fundamental method is to identify which types of proteins interact with the lncRNAs. However, the models or rules of interactions are a major challenge when calculating and estimating the types of RBP.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>In this study, we propose an ensemble deep learning model to predict plant lncRNA-protein interactions using stacked denoising autoencoder and convolutional neural network based on sequence and structural information, named PRPI-SC. PRPI-SC predicts interactions between lncRNAs and proteins based on the k-mer features of RNAs and proteins. Experiments proved good results on <jats:italic>Arabidopsis thaliana<\/jats:italic> and <jats:italic>Zea mays<\/jats:italic> datasets (ATH948 and ZEA22133). The accuracy rates of ATH948 and ZEA22133 datasets were 88.9% and 82.6%, respectively. PRPI-SC also performed well on some public RNA protein interaction datasets.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusions<\/jats:title>\n                <jats:p>PRPI-SC accurately predicts the interaction between plant lncRNA and protein, which plays a guiding role in studying the function and expression of plant lncRNA. At the same time, PRPI-SC has a strong generalization ability and good prediction effect for non-plant data.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s12859-021-04328-9","type":"journal-article","created":{"date-parts":[[2021,8,24]],"date-time":"2021-08-24T16:04:04Z","timestamp":1629821044000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["PRPI-SC: an ensemble deep learning model for predicting plant lncRNA-protein interactions"],"prefix":"10.1186","volume":"22","author":[{"given":"Haoran","family":"Zhou","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jael Sanyanda","family":"Wekesa","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yushi","family":"Luan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7357-8562","authenticated-orcid":false,"given":"Jun","family":"Meng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,8,24]]},"reference":[{"issue":"6915","key":"4328_CR1","doi-asserted-by":"publisher","first-page":"563","DOI":"10.1038\/nature01266","volume":"420","author":"Y Okazaki","year":"2002","unstructured":"Okazaki Y, Furuno M, Kasukawa T, Adachi J, Bono H, Kondo S, et al. 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