{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,11]],"date-time":"2026-04-11T12:53:10Z","timestamp":1775911990136,"version":"3.50.1"},"reference-count":39,"publisher":"Springer Science and Business Media LLC","issue":"S3","license":[{"start":{"date-parts":[[2021,5,1]],"date-time":"2021-05-01T00:00:00Z","timestamp":1619827200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2021,5,12]],"date-time":"2021-05-12T00:00:00Z","timestamp":1620777600000},"content-version":"vor","delay-in-days":11,"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":["61872055"],"award-info":[{"award-number":["61872055"]}],"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":["31872116"],"award-info":[{"award-number":["31872116"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Bioinformatics"],"published-print":{"date-parts":[[2021,5]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec>\n                <jats:title>Background<\/jats:title>\n                <jats:p>Long noncoding RNAs (lncRNAs) play an important role in regulating biological activities and their prediction is significant for exploring biological processes. Long short-term memory (LSTM) and convolutional neural network (CNN) can automatically extract and learn the abstract information from the encoded RNA sequences to avoid complex feature engineering. An ensemble model learns the information from multiple perspectives and shows better performance than a single model. It is feasible and interesting that the RNA sequence is considered as sentence and image to train LSTM and CNN respectively, and then the trained models are hybridized to predict lncRNAs. Up to present, there are various predictors for lncRNAs, but few of them are proposed for plant. A reliable and powerful predictor for plant lncRNAs is necessary.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>To boost the performance of predicting lncRNAs, this paper proposes a hybrid deep learning model based on two encoding styles (PlncRNA-HDeep), which does not require prior knowledge and only uses RNA sequences to train the models for predicting plant lncRNAs. It not only learns the diversified information from RNA sequences encoded by <jats:italic>p<\/jats:italic>-nucleotide and one-hot encodings, but also takes advantages of lncRNA-LSTM proposed in our previous study and CNN. The parameters are adjusted and three hybrid strategies are tested to maximize its performance. Experiment results show that PlncRNA-HDeep is more effective than lncRNA-LSTM and CNN and obtains 97.9% sensitivity, 95.1% precision, 96.5% accuracy and 96.5% F1 score on <jats:italic>Zea mays<\/jats:italic> dataset which are better than those of several shallow machine learning methods (support vector machine, random forest, k-nearest neighbor, decision tree, naive Bayes and logistic regression) and some existing tools (CNCI, PLEK, CPC2, LncADeep and lncRNAnet).<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusions<\/jats:title>\n                <jats:p>PlncRNA-HDeep is feasible and obtains the credible predictive results. It may also provide valuable references for other related research.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s12859-020-03870-2","type":"journal-article","created":{"date-parts":[[2021,5,12]],"date-time":"2021-05-12T09:14:01Z","timestamp":1620810841000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":26,"title":["PlncRNA-HDeep: plant long noncoding RNA prediction using hybrid deep learning based on two encoding styles"],"prefix":"10.1186","volume":"22","author":[{"given":"Jun","family":"Meng","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qiang","family":"Kang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zheng","family":"Chang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yushi","family":"Luan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,5,12]]},"reference":[{"key":"3870_CR1","doi-asserted-by":"publisher","first-page":"370","DOI":"10.1186\/s12859-016-1251-y","volume":"17","author":"QZ Zhou","year":"2016","unstructured":"Zhou QZ, Zhang B, Yu QY, Zhang Z. BmncRNAdb: a comprehensive database of non-coding RNAs in the silkworm, Bombyx mori. BMC Bioinformatics. 2016;17:370.","journal-title":"BMC Bioinformatics"},{"key":"3870_CR2","doi-asserted-by":"publisher","first-page":"2","DOI":"10.3389\/fgene.2015.00002","volume":"6","author":"AF Palazzo","year":"2015","unstructured":"Palazzo AF, Lee ES. Noncoding RNA: what is functional and what is junk? Front Genet. 2015;6:2.","journal-title":"Front Genet"},{"issue":"3","key":"3870_CR3","doi-asserted-by":"publisher","first-page":"651","DOI":"10.1534\/genetics.112.146704","volume":"193","author":"JTY Kung","year":"2013","unstructured":"Kung JTY, Colognori D, Lee JT. Long noncoding RNAs: past, present, and future. Genetics. 2013;193(3):651\u201369.","journal-title":"Genetics"},{"key":"3870_CR4","doi-asserted-by":"publisher","first-page":"407","DOI":"10.1007\/s11883-014-0407-3","volume":"16","author":"B Aryal","year":"2014","unstructured":"Aryal B, Rotllan N, Fern\u00e1ndez-Hernando C. Noncoding RNAs and atherosclerosis. Curr Atherosclerosis Rep. 2014;16:407.","journal-title":"Curr Atherosclerosis Rep"},{"issue":"13","key":"3870_CR5","doi-asserted-by":"publisher","first-page":"2491","DOI":"10.1007\/s00018-016-2174-5","volume":"73","author":"SU Schmitz","year":"2016","unstructured":"Schmitz SU, Grote P, Herrmann BG. Mechanisms of long noncoding RNA function in development and disease. Cell Mol Life Sci. 2016;73(13):2491\u2013509.","journal-title":"Cell Mol Life Sci"},{"key":"3870_CR6","doi-asserted-by":"publisher","first-page":"3235","DOI":"10.1007\/s00122-020-03690-1","volume":"133","author":"X Zhou","year":"2020","unstructured":"Zhou X, Cui J, Meng J, Luan Y. Interactions and links among the noncoding RNAs in plants under stresses. Theor Appl Genet. 2020;133:3235\u201348.","journal-title":"Theor Appl Genet"},{"key":"3870_CR7","doi-asserted-by":"publisher","first-page":"799","DOI":"10.1038\/nature08618","volume":"462","author":"S Swiezewski","year":"2009","unstructured":"Swiezewski S, Liu F, Magusin A, Dean C. Cold-induced silencing by long antisense transcripts of an Arabidopsis Polycomb target. Nature. 2009;462:799\u2013802.","journal-title":"Nature"},{"key":"3870_CR8","doi-asserted-by":"publisher","first-page":"301","DOI":"10.1016\/j.gpb.2017.01.007","volume":"15","author":"J Wang","year":"2017","unstructured":"Wang J, Meng X, Dobrovolskaya OB, Orlov YL, Chen M. Non-coding RNAs and their roles in stress response in plants. Genom Proteom Bioinf. 2017;15:301\u201312.","journal-title":"Genom Proteom Bioinf"},{"key":"3870_CR9","doi-asserted-by":"publisher","first-page":"521","DOI":"10.3390\/cells8060521","volume":"8","author":"JS Wekesa","year":"2019","unstructured":"Wekesa JS, Luan Y, Chen M, Meng J. A hybrid prediction method for plant lncRNA-protein interaction. Cells. 2019;8:521.","journal-title":"Cells"},{"issue":"11","key":"3870_CR10","doi-asserted-by":"publisher","first-page":"e1000176","DOI":"10.1371\/journal.pcbi.1000176","volume":"4","author":"ME Dinger","year":"2008","unstructured":"Dinger ME, Pang KC, Mercer TR, Mattick JS. Differentiating protein-coding and noncoding RNA: challenges and ambiguities. PLoS Comput Biol. 2008;4(11):e1000176.","journal-title":"PLoS Comput Biol"},{"key":"3870_CR11","doi-asserted-by":"publisher","first-page":"26","DOI":"10.1186\/1748-7188-6-26","volume":"6","author":"R Lorenz","year":"2011","unstructured":"Lorenz R, Bernhart SH, Siederdissen CHZ, Tafer H, Flamm C, Stadler PF, et al. ViennaRNA package 2.0. Algorithms Mol Biol. 2011;6:26.","journal-title":"Algorithms Mol Biol."},{"key":"3870_CR12","doi-asserted-by":"publisher","first-page":"90","DOI":"10.1186\/1471-2105-14-90","volume":"14","author":"C Zou","year":"2013","unstructured":"Zou C, Gong J, Li H. An improved sequence based prediction protocol for DNA-binding proteins using SVM and comprehensive feature analysis. BMC Bioinformatics. 2013;14:90.","journal-title":"BMC Bioinformatics"},{"key":"3870_CR13","doi-asserted-by":"publisher","first-page":"839","DOI":"10.1186\/s12864-018-5227-3","volume":"19","author":"Q Zhao","year":"2018","unstructured":"Zhao Q, Mao Q, Zhao Z, Dou T, Wang Z, Cui X, et al. Prediction of plant-derived xenomiRs from plant miRNA sequences using random forest and one-dimensional convolutional neural network models. BMC Genomics. 2018;19:839.","journal-title":"BMC Genomics"},{"key":"3870_CR14","doi-asserted-by":"publisher","first-page":"342","DOI":"10.1261\/rna.2164906","volume":"12","author":"E Bindewald","year":"2006","unstructured":"Bindewald E, Shapiro BA. RNA secondary structure prediction from sequence alignments using a network of k-nearest neighbor classifiers. RNA. 2006;12:342\u201352.","journal-title":"RNA"},{"issue":"17","key":"3870_CR15","doi-asserted-by":"publisher","first-page":"e166","DOI":"10.1093\/nar\/gkt646","volume":"41","author":"L Sun","year":"2013","unstructured":"Sun L, Luo H, Bu D, Zhao G, Yu K, Zhang C, et al. Utilizing sequence intrinsic composition to classify protein-coding and long noncoding transcripts. Nucleic Acids Res. 2013;41(17):e166.","journal-title":"Nucleic Acids Res"},{"key":"3870_CR16","doi-asserted-by":"publisher","first-page":"311","DOI":"10.1186\/1471-2105-15-311","volume":"15","author":"A Li","year":"2014","unstructured":"Li A, Zhang J, Zhou Z. PLEK: a tool for predicting long noncoding RNAs and messenger RNAs based on an improved k-mer scheme. BMC Bioinformatics. 2014;15:311.","journal-title":"BMC Bioinformatics"},{"key":"3870_CR17","doi-asserted-by":"publisher","first-page":"W345","DOI":"10.1093\/nar\/gkm391","volume":"35","author":"L Kong","year":"2007","unstructured":"Kong L, Zhang Y, Ye ZQ, Liu XQ, Zhao SQ, Wei L, et al. CPC: assess the protein-coding potential of transcripts using sequence features and support vector machine. Nucleic Acids Res. 2007;35:W345\u20139.","journal-title":"Nucleic Acids Res"},{"key":"3870_CR18","doi-asserted-by":"publisher","first-page":"W12","DOI":"10.1093\/nar\/gkx428","volume":"45","author":"YJ Kang","year":"2017","unstructured":"Kang YJ, Yang DC, Kong L, Hou M, Meng YQ, Wei L, et al. CPC2: a fast and accurate coding potential calculator based on sequence intrinsic features. Nucleic Acids Res. 2017;45:W12\u20136.","journal-title":"Nucleic Acids Res"},{"key":"3870_CR19","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1038\/nature14539","volume":"521","author":"Y LeCun","year":"2015","unstructured":"LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015;521:436\u201344.","journal-title":"Nature"},{"issue":"22","key":"3870_CR20","doi-asserted-by":"publisher","first-page":"3825","DOI":"10.1093\/bioinformatics\/bty428","volume":"34","author":"C Yang","year":"2018","unstructured":"Yang C, Yang L, Zhou M, Xie H, Zhang C, Wang MD, et al. LncADeep: an ab initio lncRNA identification and functional annotation tool based on deep learning. Bioinformatics. 2018;34(22):3825\u201334.","journal-title":"Bioinformatics"},{"issue":"22","key":"3870_CR21","doi-asserted-by":"publisher","first-page":"3889","DOI":"10.1093\/bioinformatics\/bty418","volume":"34","author":"J Baek","year":"2018","unstructured":"Baek J, Lee B, Kwon S, Yoon S. LncRNAnet: long non-coding RNA identification using deep learning. Bioinformatics. 2018;34(22):3889\u201397.","journal-title":"Bioinformatics"},{"issue":"3","key":"3870_CR22","doi-asserted-by":"publisher","first-page":"517","DOI":"10.1109\/TASLP.2015.2400218","volume":"23","author":"M Sundermeyer","year":"2015","unstructured":"Sundermeyer M, Ney H, Schl\u00fcter R. From feedforward to recurrent LSTM neural networks for language modeling. IEEE\/ACM Trans Audio Speech Lang Process. 2015;23(3):517\u201329.","journal-title":"IEEE\/ACM Trans Audio Speech Lang Process"},{"issue":"9","key":"3870_CR23","doi-asserted-by":"publisher","first-page":"1901","DOI":"10.1109\/TPAMI.2015.2491929","volume":"38","author":"Y Wei","year":"2016","unstructured":"Wei Y, Xia W, Lin M, Huang J, Ni B, Dong J, et al. HCP: a flexible CNN framework for multi-label image classification. IEEE Trans Pattern Anal Mach Intell. 2016;38(9):1901\u20137.","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"3870_CR24","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1016\/j.neucom.2018.02.097","volume":"324","author":"L Zhang","year":"2019","unstructured":"Zhang L, Yu G, Xia D, Wang J. Protein\u2013protein interactions prediction based on ensemble deep neural networks. Neurocomputing. 2019;324:10\u20139.","journal-title":"Neurocomputing"},{"key":"3870_CR25","doi-asserted-by":"publisher","first-page":"33","DOI":"10.1016\/j.inffus.2017.12.001","volume":"44","author":"JM Moyano","year":"2018","unstructured":"Moyano JM, Gibaja EL, Cios KJ, Ventura S. Review of ensembles of multi-label classifiers: models, experimental study and prospects. Inform Fusion. 2018;44:33\u201345.","journal-title":"Inform Fusion"},{"issue":"10","key":"3870_CR26","doi-asserted-by":"publisher","first-page":"1593","DOI":"10.4161\/rna.26312","volume":"10","author":"H Zhang","year":"2013","unstructured":"Zhang H, He X, Zhu JK. RNA-directed DNA methylation in plants. RNA Biol. 2013;10(10):1593\u20136.","journal-title":"RNA Biol"},{"key":"3870_CR27","doi-asserted-by":"publisher","first-page":"804","DOI":"10.1186\/s12864-017-4178-4","volume":"18","author":"HW Schneider","year":"2017","unstructured":"Schneider HW, Raiol T, Brigido MM, Walter MEMT, Stadler PF. A support vector machine based method to distinguish long noncoding RNAs from protein coding transcripts. BMC Genomics. 2017;18:804.","journal-title":"BMC Genomics"},{"issue":"10\u201312","key":"3870_CR28","doi-asserted-by":"publisher","first-page":"1709","DOI":"10.1016\/j.camwa.2005.05.009","volume":"50","author":"RJ Kuo","year":"2005","unstructured":"Kuo RJ, Wang HS, Hu TL, Chou SH. Application of ant K-means on clustering analysis. Comput Math Appl. 2005;50(10\u201312):1709\u201324.","journal-title":"Comput Math Appl"},{"key":"3870_CR29","doi-asserted-by":"crossref","unstructured":"Meng J, Chang Z, Zhang P, Shi W, Luan Y. lncRNA-LSTM: prediction of plant long non-coding RNAs using long short-term memory based on p-nts encoding. In: Proceedings of the 15th international conference on intelligent computing; 2019. p. 347\u201357.","DOI":"10.1007\/978-3-030-26766-7_32"},{"key":"3870_CR30","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1016\/j.imavis.2018.04.004","volume":"75","author":"P Rodr\u00edguez","year":"2018","unstructured":"Rodr\u00edguez P, Bautista MA, Gonz\u00e0lez J, Escalera S. Beyond one-hot encoding: lower dimensional target embedding. Image Vision Comput. 2018;75:21\u201331.","journal-title":"Image Vision Comput"},{"issue":"Suppl 19","key":"3870_CR31","doi-asserted-by":"publisher","first-page":"525","DOI":"10.1186\/s12859-018-2525-3","volume":"19","author":"L Zhang","year":"2018","unstructured":"Zhang L, Yu G, Guo M, Wang J. Predicting protein-protein interactions using high-quality non-interacting pairs. BMC Bioinformatics. 2018;19(Suppl 19):525.","journal-title":"BMC Bioinformatics"},{"key":"3870_CR32","doi-asserted-by":"publisher","first-page":"364","DOI":"10.1186\/s12864-017-3749-8","volume":"18","author":"Y Harigaya","year":"2017","unstructured":"Harigaya Y, Parker R. The link between adjacent codon pairs and mRNA stability. BMC Genomics. 2017;18:364.","journal-title":"BMC Genomics"},{"key":"3870_CR33","doi-asserted-by":"publisher","first-page":"D1161","DOI":"10.1093\/nar\/gkv1215","volume":"44","author":"AP Gallart","year":"2016","unstructured":"Gallart AP, Pulido AH, Lagr\u00e1n IAMD, Sanseverino W, Cigliano RA. GREENC: a wiki-based database of plant lncRNAs. Nucleic Acids Res. 2016;44:D1161\u20136.","journal-title":"Nucleic Acids Res"},{"issue":"8","key":"3870_CR34","doi-asserted-by":"publisher","first-page":"1161","DOI":"10.1109\/LSP.2015.2389852","volume":"22","author":"J Ryu","year":"2015","unstructured":"Ryu J, Koo HI, Cho NI. Word segmentation method for handwritten documents based on structured learning. IEEE Signal Proc Let. 2015;22(8):1161\u20135.","journal-title":"IEEE Signal Proc Let"},{"issue":"3","key":"3870_CR35","doi-asserted-by":"publisher","first-page":"428","DOI":"10.1016\/j.molcel.2018.06.034","volume":"71","author":"X Li","year":"2018","unstructured":"Li X, Yang L, Chen LL. The biogenesis, functions, challenges of circular RNAs. Mol Cell. 2018;71(3):428\u201342.","journal-title":"Mol Cell"},{"key":"3870_CR36","doi-asserted-by":"publisher","first-page":"1235","DOI":"10.1162\/neco_a_01199","volume":"31","author":"Y Yu","year":"2019","unstructured":"Yu Y, Si X, Hu C, Zhang J. A review of recurrent neural networks: LSTM cells and network architectures. Neural Comput. 2019;31:1235\u201370.","journal-title":"Neural Comput"},{"key":"3870_CR37","doi-asserted-by":"publisher","first-page":"469","DOI":"10.1186\/s12859-019-3039-3","volume":"20","author":"J Wen","year":"2019","unstructured":"Wen J, Liu Y, Shi Y, Huang H, Deng B, Xiao X. A classification model for lncRNA and mRNA based on k-mers and a convolutional neural network. BMC Bioinformatics. 2019;20:469.","journal-title":"BMC Bioinformatics"},{"key":"3870_CR38","doi-asserted-by":"publisher","first-page":"82","DOI":"10.1007\/s12539-019-00351-w","volume":"12","author":"P Zhang","year":"2020","unstructured":"Zhang P, Meng J, Luan Y, Liu C. Plant miRNA-lncRNA interaction prediction with the ensemble of CNN and IndRNN. Interdiscip Sci. 2020;12:82\u20139.","journal-title":"Interdiscip Sci"},{"key":"3870_CR39","doi-asserted-by":"publisher","first-page":"285","DOI":"10.1007\/s10115-012-0538-1","volume":"35","author":"AK Farahat","year":"2013","unstructured":"Farahat AK, Ghodsi A, Kamel MS. Efficient greedy feature selection for unsupervised learning. Knowl Inf Syst. 2013;35:285\u2013310.","journal-title":"Knowl Inf Syst"}],"container-title":["BMC Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12859-020-03870-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12859-020-03870-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12859-020-03870-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,5,12]],"date-time":"2021-05-12T09:14:33Z","timestamp":1620810873000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcbioinformatics.biomedcentral.com\/articles\/10.1186\/s12859-020-03870-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,5]]},"references-count":39,"journal-issue":{"issue":"S3","published-print":{"date-parts":[[2021,5]]}},"alternative-id":["3870"],"URL":"https:\/\/doi.org\/10.1186\/s12859-020-03870-2","relation":{},"ISSN":["1471-2105"],"issn-type":[{"value":"1471-2105","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,5]]},"assertion":[{"value":"29 October 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 November 2020","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 May 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"Not applicable.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare that they have no competing interests.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"242"}}