{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,10]],"date-time":"2025-11-10T13:48:47Z","timestamp":1762782527481,"version":"3.37.3"},"reference-count":74,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2021,1,18]],"date-time":"2021-01-18T00:00:00Z","timestamp":1610928000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2021,1,18]],"date-time":"2021-01-18T00:00:00Z","timestamp":1610928000000},"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":["61772197"],"award-info":[{"award-number":["61772197"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Key Research and Development Program of China","award":["2018YFC0910404"],"award-info":[{"award-number":["2018YFC0910404"]}]},{"name":"Beijing Natural Science Foundation","award":["4192044"],"award-info":[{"award-number":["4192044"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Bioinformatics"],"abstract":"<jats:title>Abstract<\/jats:title><jats:sec>\n                <jats:title>Background<\/jats:title>\n                <jats:p>Long non-coding RNAs (lncRNAs) regulate diverse biological processes via interactions with proteins. Since the experimental methods to identify these interactions are expensive and time-consuming, many computational methods have been proposed. Although these computational methods have achieved promising prediction performance, they neglect the fact that a gene may encode multiple protein isoforms and different isoforms of the same gene may interact differently with the same lncRNA.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>In this study, we propose a novel method, DeepLPI, for predicting the interactions between lncRNAs and protein isoforms. Our method uses sequence and structure data to extract intrinsic features and expression data to extract topological features. To combine these different data, we adopt a hybrid framework by integrating a multimodal deep learning neural network and a conditional random field. To overcome the lack of known interactions between lncRNAs and protein isoforms, we apply a multiple instance learning (MIL) approach. In our experiment concerning the human lncRNA-protein interactions in the NPInter v3.0 database, DeepLPI improved the prediction performance by 4.7% in term of AUC and 5.9% in term of AUPRC over the state-of-the-art methods. Our further correlation analyses between interactive lncRNAs and protein isoforms also illustrated that their co-expression information helped predict the interactions. Finally, we give some examples where DeepLPI was able to outperform the other methods in predicting mouse lncRNA-protein interactions and novel human lncRNA-protein interactions.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusion<\/jats:title>\n                <jats:p>Our results demonstrated that the use of isoforms and MIL contributed significantly to the improvement of performance in predicting lncRNA and protein interactions. We believe that such an approach would find more applications in predicting other functional roles of RNAs and proteins.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s12859-020-03914-7","type":"journal-article","created":{"date-parts":[[2021,1,18]],"date-time":"2021-01-18T14:03:29Z","timestamp":1610978609000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["DeepLPI: a multimodal deep learning method for predicting the interactions between lncRNAs and protein isoforms"],"prefix":"10.1186","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9556-0243","authenticated-orcid":false,"given":"Dipan","family":"Shaw","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hao","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Minzhu","family":"Xie","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tao","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,1,18]]},"reference":[{"issue":"13","key":"3914_CR1","doi-asserted-by":"publisher","first-page":"1494","DOI":"10.1101\/gad.1800909","volume":"23","author":"JE Wilusz","year":"2009","unstructured":"Wilusz JE, Sunwoo H, Spector DL. Long noncoding RNAs: functional surprises from the RNA world. Genes Dev. 2009;23(13):1494\u2013504. https:\/\/doi.org\/10.1101\/gad.1800909.","journal-title":"Genes Dev"},{"key":"3914_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. Non-coding RNA: what is functional and what is junk? Front Genet. 2015;6:2.","journal-title":"Front Genet"},{"issue":"6","key":"3914_CR3","doi-asserted-by":"publisher","first-page":"1284","DOI":"10.3390\/ijms20061284","volume":"20","author":"H Zhang","year":"2019","unstructured":"Zhang H, Liang Y, Han S, Peng C, Li Y. Long noncoding RNA and protein interactions: from experimental results to computational models based on network methods. Int J Mol Sci. 2019;20(6):1284.","journal-title":"Int J Mol Sci"},{"issue":"18","key":"3914_CR4","doi-asserted-by":"publisher","first-page":"3101","DOI":"10.1093\/bioinformatics\/bty208","volume":"34","author":"AR Gawronski","year":"2018","unstructured":"Gawronski AR, Uhl M, Zhang Y, Lin Y-Y, Niknafs YS, Ramnarine VR, Malik R, Feng F, Chinnaiyan AM, Collins CC, et al. MechRNA: prediction of lncRNA mechanisms from RNA-RNA and RNA-protein interactions. Bioinformatics. 2018;34(18):3101\u201310.","journal-title":"Bioinformatics"},{"issue":"3","key":"3914_CR5","doi-asserted-by":"publisher","first-page":"393","DOI":"10.1016\/j.cell.2018.01.011","volume":"172","author":"F Kopp","year":"2018","unstructured":"Kopp F, Mendell JT. Functional classification and experimental dissection of long noncoding RNAs. Cell. 2018;172(3):393\u2013407. https:\/\/doi.org\/10.1016\/j.cell.2018.01.011.","journal-title":"Cell"},{"issue":"1","key":"3914_CR6","doi-asserted-by":"publisher","first-page":"129","DOI":"10.1016\/j.cell.2010.03.009","volume":"141","author":"M Hafner","year":"2010","unstructured":"Hafner M, Landthaler M, Burger L, Khorshid M, Hausser J, Berninger P, Rothballer A, Ascano M Jr, Jungkamp A-C, Munschauer M, Ulrich A, Wardle GS, Dewell S, Zavolan M, Tuschl T. Transcriptome-wide identification of RNA-binding protein and microRNA target sites by par-clip. Cell. 2010;141(1):129\u201341. https:\/\/doi.org\/10.1016\/j.cell.2010.03.009.","journal-title":"Cell"},{"issue":"7","key":"3914_CR7","doi-asserted-by":"publisher","first-page":"667","DOI":"10.1038\/nbt.1550","volume":"27","author":"D Ray","year":"2009","unstructured":"Ray D, Kazan H, Chan ET, Pe\u00f1a Castillo L, Chaudhry S, Talukder S, Blencowe BJ, Morris Q, Hughes TR. Rapid and systematic analysis of the RNA recognition specificities of RNA-binding proteins. Nat Biotechnol. 2009;27(7):667\u201370. https:\/\/doi.org\/10.1038\/nbt.1550.","journal-title":"Nat Biotechnol"},{"issue":"7221","key":"3914_CR8","doi-asserted-by":"publisher","first-page":"464","DOI":"10.1038\/nature07488","volume":"456","author":"DD Licatalosi","year":"2008","unstructured":"Licatalosi DD, Mele A, Fak JJ, Ule J, Kayikci M, Chi SW, Clark TA, Schweitzer AC, Blume JE, Wang X, Darnell JC, Darnell RB. Hits-clip yields genome-wide insights into brain alternative RNA processing. Nature. 2008;456(7221):464\u20139. https:\/\/doi.org\/10.1038\/nature07488.","journal-title":"Nature"},{"issue":"1","key":"3914_CR9","doi-asserted-by":"publisher","first-page":"302","DOI":"10.1038\/nprot.2006.47","volume":"1","author":"JD Keene","year":"2006","unstructured":"Keene JD, Komisarow JM, Friedersdorf MB. Rip-chip: the isolation and identification of mRNAs, microRNAs and protein components of ribonucleoprotein complexes from cell extracts. Nat Protoc. 2006;1(1):302\u20137. https:\/\/doi.org\/10.1038\/nprot.2006.47.","journal-title":"Nat Protoc"},{"issue":"1","key":"3914_CR10","doi-asserted-by":"publisher","first-page":"489","DOI":"10.1186\/1471-2105-12-489","volume":"12","author":"UK Muppirala","year":"2011","unstructured":"Muppirala UK, Honavar VG, Dobbs D. Predicting RNA-protein interactions using only sequence information. BMC Bioinform. 2011;12(1):489.","journal-title":"BMC Bioinform"},{"issue":"6","key":"3914_CR11","doi-asserted-by":"publisher","first-page":"444","DOI":"10.1038\/nmeth.1611","volume":"8","author":"M Bellucci","year":"2011","unstructured":"Bellucci M, Agostini F, Masin M, Tartaglia GG. Predicting protein associations with long noncoding RNAs. Nat Methods. 2011;8(6):444.","journal-title":"Nat Methods"},{"issue":"1","key":"3914_CR12","doi-asserted-by":"publisher","first-page":"133","DOI":"10.1039\/C2MB25292A","volume":"9","author":"Y Wang","year":"2013","unstructured":"Wang Y, Chen X, Liu Z-P, Huang Q, Wang Y, Xu D, Zhang X-S, Chen R, Chen L. De novo prediction of RNA-protein interactions from sequence information. Mol BioSyst. 2013;9(1):133\u201342.","journal-title":"Mol BioSyst"},{"issue":"1","key":"3914_CR13","doi-asserted-by":"publisher","first-page":"651","DOI":"10.1186\/1471-2164-14-651","volume":"14","author":"Q Lu","year":"2013","unstructured":"Lu Q, Ren S, Lu M, Zhang Y, Zhu D, Zhang X, Li T. Computational prediction of associations between long non-coding RNAs and proteins. BMC Genom. 2013;14(1):651.","journal-title":"BMC Genom"},{"issue":"3","key":"3914_CR14","doi-asserted-by":"publisher","first-page":"1370","DOI":"10.1093\/nar\/gkv020","volume":"43","author":"V Suresh","year":"2015","unstructured":"Suresh V, Liu L, Adjeroh D, Zhou X. Rpi-pred: predicting ncRNA-protein interaction using sequence and structural information. Nucleic Acids Res. 2015;43(3):1370\u20139.","journal-title":"Nucleic Acids Res"},{"key":"3914_CR15","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.jtbi.2016.04.025","volume":"402","author":"M Akbaripour-Elahabad","year":"2016","unstructured":"Akbaripour-Elahabad M, Zahiri J, Rafeh R, Eslami M, Azari M. rpicool: A tool for in silico RNA-protein interaction detection using random forest. J Theor Biol. 2016;402:1\u20138.","journal-title":"J Theor Biol"},{"key":"3914_CR16","doi-asserted-by":"publisher","first-page":"88","DOI":"10.1016\/j.neucom.2019.08.084","volume":"370","author":"X-N Fan","year":"2019","unstructured":"Fan X-N, Zhang S-W. Lpi-bls: predicting lncRNA-protein interactions with a broad learning system-based stacked ensemble classifier. Neurocomputing. 2019;370:88\u201393.","journal-title":"Neurocomputing"},{"issue":"1","key":"3914_CR17","doi-asserted-by":"publisher","first-page":"582","DOI":"10.1186\/s12864-016-2931-8","volume":"17","author":"X Pan","year":"2016","unstructured":"Pan X, Fan Y-X, Yan J, Shen H-B. Ipminer: hidden ncRNA-protein interaction sequential pattern mining with stacked autoencoder for accurate computational prediction. BMC Genom. 2016;17(1):582.","journal-title":"BMC Genom"},{"key":"3914_CR18","doi-asserted-by":"publisher","first-page":"337","DOI":"10.1016\/j.omtn.2018.03.001","volume":"11","author":"H-C Yi","year":"2018","unstructured":"Yi H-C, You Z-H, Huang D-S, Li X, Jiang T-H, Li L-P. A deep learning framework for robust and accurate prediction of ncRNA-protein interactions using evolutionary information. Mol Ther Nucleic Acids. 2018;11:337\u201344.","journal-title":"Mol Ther Nucleic Acids"},{"issue":"5","key":"3914_CR19","doi-asserted-by":"publisher","first-page":"1070","DOI":"10.3390\/ijms20051070","volume":"20","author":"C Peng","year":"2019","unstructured":"Peng C, Han S, Zhang H, Li Y. Rpiter: a hierarchical deep learning framework for ncRNA-protein interaction prediction. Int J Mol Sci. 2019;20(5):1070.","journal-title":"Int J Mol Sci"},{"issue":"22","key":"3914_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, Zhu H. Lncadeep: an ab initio lncRNA identification and functional annotation tool based on deep learning. Bioinformatics. 2018;34(22):3825\u201334.","journal-title":"Bioinformatics"},{"issue":"1","key":"3914_CR21","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12859-020-3406-0","volume":"21","author":"H-C Yi","year":"2020","unstructured":"Yi H-C, You Z-H, Wang M-N, Guo Z-H, Wang Y-B, Zhou J-R. Rpi-se: a stacking ensemble learning framework for ncRNA-protein interactions prediction using sequence information. BMC Bioinform. 2020;21(1):1\u201310.","journal-title":"BMC Bioinform"},{"key":"3914_CR22","doi-asserted-by":"publisher","first-page":"230","DOI":"10.1016\/j.jtbi.2018.10.029","volume":"461","author":"L Wang","year":"2019","unstructured":"Wang L, Yan X, Liu M-L, Song K-J, Sun X-F, Pan W-W. Prediction of RNA-protein interactions by combining deep convolutional neural network with feature selection ensemble method. J Theor Biol. 2019;461:230\u20138.","journal-title":"J Theor Biol"},{"issue":"4","key":"3914_CR23","doi-asserted-by":"publisher","first-page":"978","DOI":"10.3390\/ijms20040978","volume":"20","author":"Z-H Zhan","year":"2019","unstructured":"Zhan Z-H, Jia L-N, Zhou Y, Li L-P, Yi H-C. Bgfe: a deep learning model for ncRNA-protein interaction predictions based on improved sequence information. Int J Mol Sci. 2019;20(4):978.","journal-title":"Int J Mol Sci"},{"key":"3914_CR24","doi-asserted-by":"publisher","first-page":"107088","DOI":"10.1016\/j.compbiolchem.2019.107088","volume":"83","author":"S Cheng","year":"2019","unstructured":"Cheng S, Zhang L, Tan J, Gong W, Li C, Zhang X. Dm-rpis: predicting ncRNA-protein interactions using stacked ensembling strategy. Comput Biol Chem. 2019;83:107088.","journal-title":"Comput Biol Chem"},{"doi-asserted-by":"crossref","unstructured":"Li A, Ge M, Zhang Y, Peng C, Wang M. Predicting long noncoding RNA and protein interactions using heterogeneous network model. BioMed Res Int. 2015;2015.","key":"3914_CR25","DOI":"10.1155\/2015\/671950"},{"issue":"1","key":"3914_CR26","doi-asserted-by":"publisher","first-page":"62","DOI":"10.1016\/j.gpb.2016.01.004","volume":"14","author":"M Ge","year":"2016","unstructured":"Ge M, Li A, Wang M. A bipartite network-based method for prediction of long non-coding RNA-protein interactions. Genom Proteom Bioinform. 2016;14(1):62\u201371.","journal-title":"Genom Proteom Bioinform"},{"issue":"1","key":"3914_CR27","doi-asserted-by":"publisher","first-page":"3664","DOI":"10.1038\/s41598-017-03986-1","volume":"7","author":"Y Xiao","year":"2017","unstructured":"Xiao Y, Zhang J, Deng L. Prediction of lncRNA-protein interactions using hetesim scores based on heterogeneous networks. Sci Rep. 2017;7(1):3664.","journal-title":"Sci Rep"},{"issue":"10","key":"3914_CR28","doi-asserted-by":"publisher","first-page":"2479","DOI":"10.1109\/TKDE.2013.2297920","volume":"26","author":"C Shi","year":"2014","unstructured":"Shi C, Kong X, Huang Y, Philip SY, Wu B. Hetesim: a general framework for relevance measure in heterogeneous networks. IEEE Trans Knowl Data Eng. 2014;26(10):2479\u201392.","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"9","key":"3914_CR29","doi-asserted-by":"publisher","first-page":"1781","DOI":"10.1039\/C7MB00290D","volume":"13","author":"H Hu","year":"2017","unstructured":"Hu H, Zhu C, Ai H, Zhang L, Zhao J, Zhao Q, Liu H. Lpi-etslp: lncRNA-protein interaction prediction using eigenvalue transformation-based semi-supervised link prediction. Mol BioSyst. 2017;13(9):1781\u20137.","journal-title":"Mol BioSyst"},{"key":"3914_CR30","doi-asserted-by":"publisher","first-page":"526","DOI":"10.1016\/j.neucom.2017.07.065","volume":"273","author":"W Zhang","year":"2018","unstructured":"Zhang W, Qu Q, Zhang Y, Wang W. The linear neighborhood propagation method for predicting long non-coding RNA-protein interactions. Neurocomputing. 2018;273:526\u201334.","journal-title":"Neurocomputing"},{"key":"3914_CR31","doi-asserted-by":"publisher","first-page":"239","DOI":"10.3389\/fgene.2018.00239","volume":"9","author":"Q Zhao","year":"2018","unstructured":"Zhao Q, Zhang Y, Hu H, Ren G, Zhang W, Liu H. Irwnrlpi: integrating random walk and neighborhood regularized logistic matrix factorization for lncRNA-protein interaction prediction. Front Genet. 2018;9:239.","journal-title":"Front Genet"},{"issue":"1","key":"3914_CR32","doi-asserted-by":"publisher","first-page":"370","DOI":"10.1186\/s12859-018-2390-0","volume":"19","author":"L Deng","year":"2018","unstructured":"Deng L, Wang J, Xiao Y, Wang Z, Liu H. Accurate prediction of protein-lncRNA interactions by diffusion and hetesim features across heterogeneous network. BMC Bioinform. 2018;19(1):370.","journal-title":"BMC Bioinform"},{"issue":"12","key":"3914_CR33","doi-asserted-by":"publisher","first-page":"1006616","DOI":"10.1371\/journal.pcbi.1006616","volume":"14","author":"W Zhang","year":"2018","unstructured":"Zhang W, Yue X, Tang G, Wu W, Huang F, Zhang X. Sfpel-lpi: sequence-based feature projection ensemble learning for predicting lncRNA-protein interactions. PLoS Comput Biol. 2018;14(12):1006616.","journal-title":"PLoS Comput Biol"},{"key":"3914_CR34","doi-asserted-by":"publisher","first-page":"464","DOI":"10.1016\/j.omtn.2018.09.020","volume":"13","author":"Q Zhao","year":"2018","unstructured":"Zhao Q, Yu H, Ming Z, Hu H, Ren G, Liu H. The bipartite network projection-recommended algorithm for predicting long non-coding RNA-protein interactions. Mol Ther Nucleic Acids. 2018;13:464\u201371.","journal-title":"Mol Ther Nucleic Acids"},{"key":"3914_CR35","doi-asserted-by":"publisher","first-page":"13486","DOI":"10.1109\/ACCESS.2019.2894225","volume":"7","author":"C Shen","year":"2019","unstructured":"Shen C, Ding Y, Tang J, Jiang L, Guo F. Lpi-ktaslp: prediction of lncRNA-protein interaction by semi-supervised link learning with multivariate information. IEEE Access. 2019;7:13486\u201396.","journal-title":"IEEE Access"},{"key":"3914_CR36","doi-asserted-by":"publisher","first-page":"343","DOI":"10.3389\/fgene.2019.00343","volume":"10","author":"G Xie","year":"2019","unstructured":"Xie G, Wu C, Sun Y, Fan Z, Liu J. Lpi-ibnra: long non-coding RNA-protein interaction prediction based on improved bipartite network recommender algorithm. Front Genet. 2019;10:343.","journal-title":"Front Genet"},{"issue":"2","key":"3914_CR37","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1186\/1471-2164-16-S2-S10","volume":"16","author":"Y-T Tseng","year":"2015","unstructured":"Tseng Y-T, Li W, Chen C-H, Zhang S, Chen JJ, Zhou XJ, Liu C-C. Iiidb: a database for isoform-isoform interactions and isoform network modules. BMC Genom. 2015;16(2):10.","journal-title":"BMC Genom"},{"unstructured":"Ngiam J, Khosla A, Kim M, Nam J, Lee H, Ng AY. Multimodal deep learning. In: Proceedings of the 28th international conference on machine learning (ICML-11), pp. 689\u2013696 (2011)","key":"3914_CR38"},{"issue":"14","key":"3914_CR39","doi-asserted-by":"publisher","first-page":"284","DOI":"10.1093\/bioinformatics\/btz367","volume":"35","author":"H Chen","year":"2019","unstructured":"Chen H, Shaw D, Zeng J, Bu D, Jiang T. Diffuse: predicting isoform functions from sequences and expression profiles via deep learning. Bioinformatics. 2019;35(14):284\u201394.","journal-title":"Bioinformatics"},{"unstructured":"Andrews S, Hofmann T, Tsochantaridis I. Multiple instance learning with generalized support vector machines. In: AAAI\/IAAI, pp. 943\u2013944 (2002)","key":"3914_CR40"},{"key":"3914_CR41","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1016\/j.patcog.2017.08.026","volume":"74","author":"X Wang","year":"2018","unstructured":"Wang X, Yan Y, Tang P, Bai X, Liu W. Revisiting multiple instance neural networks. Pattern Recogn. 2018;74:15\u201324.","journal-title":"Pattern Recogn"},{"doi-asserted-by":"crossref","unstructured":"Hao Y, Wu W, Li H, Yuan J, Luo J, Zhao Y, Chen R. Npinter v3. 0: an upgraded database of noncoding RNA-associated interactions. Database 2016 (2016)","key":"3914_CR42","DOI":"10.1093\/database\/baw057"},{"doi-asserted-by":"crossref","unstructured":"Yang J, Li A, Ge M, Wang M. Prediction of interactions between lncRNA and protein by using relevance search in a heterogeneous lncRNA-protein network. In: 2015 34th Chinese Control Conference (Ccc), pp. 8540\u20138544 (2015). IEEE","key":"3914_CR43","DOI":"10.1109\/ChiCC.2015.7260990"},{"issue":"1","key":"3914_CR44","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s42113-018-0011-7","volume":"2","author":"QF Gronau","year":"2019","unstructured":"Gronau QF, Wagenmakers E-J. Limitations of bayesian leave-one-out cross-validation for model selection. Comput Brain Behav. 2019;2(1):1\u201311.","journal-title":"Comput Brain Behav"},{"issue":"D1","key":"3914_CR45","doi-asserted-by":"publisher","first-page":"309","DOI":"10.1093\/nar\/gky1085","volume":"47","author":"J Huerta-Cepas","year":"2019","unstructured":"Huerta-Cepas J, Szklarczyk D, Heller D, Hern\u00e1ndez-Plaza A, Forslund SK, Cook H, Mende DR, Letunic I, Rattei T, Jensen LJ, et al. eggNOG 5.0: a hierarchical, functionally and phylogenetically annotated orthology resource based on 5090 organisms and 2502 viruses. Nucleic Acids Res. 2019;47(D1):309\u201314.","journal-title":"Nucleic Acids Res"},{"unstructured":"Simonyan K, Vedaldi A, Zisserman A. Deep inside convolutional networks: Visualising image classification models and saliency maps. arXiv preprint arXiv:1312.6034 (2013)","key":"3914_CR46"},{"issue":"7485","key":"3914_CR47","doi-asserted-by":"publisher","first-page":"706","DOI":"10.1038\/nature12946","volume":"505","author":"Y Wan","year":"2014","unstructured":"Wan Y, Qu K, Zhang QC, Flynn RA, Manor O, Ouyang Z, Zhang J, Spitale RC, Snyder MP, Segal E, et al. Landscape and variation of RNA secondary structure across the human transcriptome. Nature. 2014;505(7485):706.","journal-title":"Nature"},{"issue":"15","key":"3914_CR48","doi-asserted-by":"publisher","first-page":"2235","DOI":"10.1093\/bioinformatics\/btu144","volume":"30","author":"H Caniza","year":"2014","unstructured":"Caniza H, Romero AE, Heron S, Yang H, Devoto A, Frasca M, Mesiti M, Valentini G, Paccanaro A. Gossto: a stand-alone application and a web tool for calculating semantic similarities on the gene ontology. Bioinformatics. 2014;30(15):2235\u20136.","journal-title":"Bioinformatics"},{"issue":"15","key":"3914_CR49","doi-asserted-by":"publisher","first-page":"2535","DOI":"10.1093\/bioinformatics\/bty1017","volume":"35","author":"D Shaw","year":"2018","unstructured":"Shaw D, Chen H, Jiang T. Deepisofun: a deep domain adaptation approach to predict isoform functions. Bioinformatics. 2018;35(15):2535\u201344.","journal-title":"Bioinformatics."},{"issue":"3","key":"3914_CR50","doi-asserted-by":"publisher","first-page":"1063","DOI":"10.1016\/j.bbagen.2013.10.035","volume":"1840","author":"P Johnsson","year":"2014","unstructured":"Johnsson P, Lipovich L, Grand\u00e9r D, Morris KV. Evolutionary conservation of long non-coding RNAs; sequence, structure, function. Biochimica et Biophysica Acta (BBA) Gen Subj. 2014;1840(3):1063\u201371.","journal-title":"Biochimica et Biophysica Acta (BBA) Gen Subj"},{"issue":"14","key":"3914_CR51","doi-asserted-by":"publisher","first-page":"489","DOI":"10.1186\/s12859-017-1890-7","volume":"18","author":"D Li","year":"2017","unstructured":"Li D, Yang MQ. Identification and characterization of conserved lncRNAs in human and rat brain. BMC Bioinform. 2017;18(14):489.","journal-title":"BMC Bioinform"},{"issue":"1","key":"3914_CR52","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1186\/s13287-018-0813-5","volume":"9","author":"J Tu","year":"2018","unstructured":"Tu J, Tian G, Cheung H-H, Wei W, Lee T-l. Gas5 is an essential lncRNA regulator for self-renewal and pluripotency of mouse embryonic stem cells and induced pluripotent stem cells. Stem Cell Res Ther. 2018;9(1):71.","journal-title":"Stem Cell Res Ther"},{"issue":"1","key":"3914_CR53","doi-asserted-by":"publisher","first-page":"2798","DOI":"10.1038\/s41598-018-21105-6","volume":"8","author":"N Pospiech","year":"2018","unstructured":"Pospiech N, Cibis H, Dietrich L, M\u00fcller F, Bange T, Hennig S. Identification of novel pandar protein interaction partners involved in splicing regulation. Sci Rep. 2018;8(1):2798.","journal-title":"Sci Rep"},{"doi-asserted-by":"crossref","unstructured":"Zhang M, Gu Y, Su M, Zhang S, Chen C, Lv W, Zhang Y. Inferring novel lncRNA associated with ventricular septal defect by dna methylation interaction network. BioRxiv. 2018;459677.","key":"3914_CR54","DOI":"10.1101\/459677"},{"issue":"1","key":"3914_CR55","doi-asserted-by":"publisher","first-page":"122","DOI":"10.1186\/s12935-018-0621-0","volume":"18","author":"X Yin","year":"2018","unstructured":"Yin X, Huang S, Zhu R, Fan F, Sun C, Hu Y. Identification of long non-coding RNA competing interactions and biological pathways associated with prognosis in pediatric and adolescent cytogenetically normal acute myeloid leukemia. Cancer Cell Int. 2018;18(1):122.","journal-title":"Cancer Cell Int"},{"issue":"5","key":"3914_CR56","first-page":"2365","volume":"39","author":"Y Xing","year":"2018","unstructured":"Xing Y, Zhao Z, Zhu Y, Zhao L, Zhu A, Piao D. Comprehensive analysis of differential expression profiles of mRNAs and lncRNAs and identification of a 14-lncRNA prognostic signature for patients with colon adenocarcinoma. Oncol Rep. 2018;39(5):2365\u201375.","journal-title":"Oncol Rep"},{"key":"3914_CR57","doi-asserted-by":"publisher","first-page":"249","DOI":"10.1016\/j.neunet.2018.07.011","volume":"106","author":"M Buda","year":"2018","unstructured":"Buda M, Maki A, Mazurowski MA. A systematic study of the class imbalance problem in convolutional neural networks. Neural Netw. 2018;106:249\u201359.","journal-title":"Neural Netw"},{"issue":"9","key":"3914_CR58","doi-asserted-by":"publisher","first-page":"1760","DOI":"10.1101\/gr.135350.111","volume":"22","author":"J Harrow","year":"2012","unstructured":"Harrow J, Frankish A, Gonzalez JM, Tapanari E, Diekhans M, Kokocinski F, Aken BL, Barrell D, Zadissa A, Searle S, et al. Gencode: the reference human genome annotation for the encode project. Genome Res. 2012;22(9):1760\u201374.","journal-title":"Genome Res"},{"issue":"1","key":"3914_CR59","doi-asserted-by":"publisher","first-page":"38","DOI":"10.1093\/nar\/30.1.38","volume":"30","author":"T Hubbard","year":"2002","unstructured":"Hubbard T, Barker D, Birney E, Cameron G, Chen Y, Clark L, Cox T, Cuff J, Curwen V, Down T, et al. The ensembl genome database project. Nucleic Acids Res. 2002;30(1):38\u201341.","journal-title":"Nucleic Acids Res"},{"issue":"4","key":"3914_CR60","doi-asserted-by":"publisher","first-page":"500","DOI":"10.1093\/bioinformatics\/btk010","volume":"22","author":"P Steffen","year":"2005","unstructured":"Steffen P, Vo\u00df B, Rehmsmeier M, Reeder J, Giegerich R. RNAshapes: an integrated RNA analysis package based on abstract shapes. Bioinformatics. 2005;22(4):500\u20133.","journal-title":"Bioinformatics"},{"issue":"1","key":"3914_CR61","doi-asserted-by":"publisher","first-page":"511","DOI":"10.1186\/s12864-018-4889-1","volume":"19","author":"X Pan","year":"2018","unstructured":"Pan X, Rijnbeek P, Yan J, Shen H-B. Prediction of RNA-protein sequence and structure binding preferences using deep convolutional and recurrent neural networks. BMC Genom. 2018;19(1):511.","journal-title":"BMC Genom"},{"doi-asserted-by":"crossref","unstructured":"Yang Y, Heffernan R, Paliwal K, Lyons J, Dehzangi A, Sharma A, Wang J, Sattar A, Zhou Y. Spider2: a package to predict secondary structure, accessible surface area, and main-chain torsional angles by deep neural networks. In: Prediction of protein secondary structure, pp. 55\u201363. Springer, Berlin (2017)","key":"3914_CR62","DOI":"10.1007\/978-1-4939-6406-2_6"},{"doi-asserted-by":"crossref","unstructured":"Zhao Z, Bai J, Wu A, Wang Y, Zhang J, Wang Z, Li Y, Xu J, Li X. Co-lncRNA: investigating the lncRNA combinatorial effects in go annotations and kegg pathways based on human RNA-seq data. Database. 2015;2015.","key":"3914_CR63","DOI":"10.1093\/database\/bav082"},{"issue":"D1","key":"3914_CR64","doi-asserted-by":"publisher","first-page":"733","DOI":"10.1093\/nar\/gkv1189","volume":"44","author":"NA O\u2019Leary","year":"2015","unstructured":"O\u2019Leary NA, Wright MW, Brister JR, Ciufo S, Haddad D, McVeigh R, Rajput B, Robbertse B, Smith-White B, Ako-Adjei D, et al. Reference sequence (refseq) database at ncbi: current status, taxonomic expansion, and functional annotation. Nucleic Acids Res. 2015;44(D1):733\u201345.","journal-title":"Nucleic Acids Res"},{"issue":"D1","key":"3914_CR65","doi-asserted-by":"publisher","first-page":"308","DOI":"10.1093\/nar\/gkx1107","volume":"46","author":"S Fang","year":"2017","unstructured":"Fang S, Zhang L, Guo J, Niu Y, Wu Y, Li H, Zhao L, Li X, Teng X, Sun X, et al. Noncodev5: a comprehensive annotation database for long non-coding RNAs. Nucleic Acids Res. 2017;46(D1):308\u201314.","journal-title":"Nucleic Acids Res"},{"issue":"4","key":"3914_CR66","doi-asserted-by":"publisher","first-page":"660","DOI":"10.1093\/bioinformatics\/btx624","volume":"34","author":"M Kulmanov","year":"2017","unstructured":"Kulmanov M, Khan MA, Hoehndorf R. Deepgo: predicting protein functions from sequence and interactions using a deep ontology-aware classifier. Bioinformatics. 2017;34(4):660\u20138.","journal-title":"Bioinformatics"},{"issue":"Feb","key":"3914_CR67","first-page":"1137","volume":"3","author":"Y Bengio","year":"2003","unstructured":"Bengio Y, Ducharme R, Vincent P, Jauvin C. A neural probabilistic language model. J Mach Learn Res. 2003;3(Feb):1137\u201355.","journal-title":"J Mach Learn Res"},{"unstructured":"Chollet F, et al.: Keras. https:\/\/keras.io (2015)","key":"3914_CR68"},{"issue":"11","key":"3914_CR69","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1093\/nar\/gkw226","volume":"44","author":"D Quang","year":"2016","unstructured":"Quang D, Xie X. Danq: a hybrid convolutional and recurrent deep neural network for quantifying the function of dna sequences. Nucleic Acids Res. 2016;44(11):107.","journal-title":"Nucleic Acids Res"},{"key":"3914_CR70","doi-asserted-by":"publisher","first-page":"40","DOI":"10.1016\/j.ymeth.2019.03.020","volume":"166","author":"D Quang","year":"2019","unstructured":"Quang D, Xie X. Factornet: a deep learning framework for predicting cell type specific transcription factor binding from nucleotide-resolution sequential data. Methods. 2019;166:40\u20137.","journal-title":"Methods"},{"issue":"1","key":"3914_CR71","doi-asserted-by":"publisher","first-page":"533","DOI":"10.1186\/s12859-018-2546-y","volume":"19","author":"R Ehsani","year":"2018","unstructured":"Ehsani R, Drabl\u00f8s F. Measures of co-expression for improved function prediction of long non-coding RNAs. BMC Bioinform. 2018;19(1):533.","journal-title":"BMC Bioinform"},{"issue":"1","key":"3914_CR72","doi-asserted-by":"publisher","first-page":"559","DOI":"10.1186\/1471-2105-9-559","volume":"9","author":"P Langfelder","year":"2008","unstructured":"Langfelder P, Horvath S. Wgcna: an r package for weighted correlation network analysis. BMC Bioinform. 2008;9(1):559.","journal-title":"BMC Bioinform"},{"doi-asserted-by":"crossref","unstructured":"Langfelder P, Horvath S. Fast R functions for robust correlations and hierarchical clustering. J Stat Softw. 2012;46(11).","key":"3914_CR73","DOI":"10.18637\/jss.v046.i11"},{"unstructured":"Kr\u00e4henb\u00fchl P, Koltun V. Efficient inference in fully connected crfs with gaussian edge potentials. In: Advances in neural information processing systems, 2011; pp. 109\u2013117.","key":"3914_CR74"}],"container-title":["BMC Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12859-020-03914-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12859-020-03914-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12859-020-03914-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,28]],"date-time":"2023-01-28T05:11:32Z","timestamp":1674882692000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcbioinformatics.biomedcentral.com\/articles\/10.1186\/s12859-020-03914-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,1,18]]},"references-count":74,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2021,12]]}},"alternative-id":["3914"],"URL":"https:\/\/doi.org\/10.1186\/s12859-020-03914-7","relation":{},"ISSN":["1471-2105"],"issn-type":[{"type":"electronic","value":"1471-2105"}],"subject":[],"published":{"date-parts":[[2021,1,18]]},"assertion":[{"value":"15 July 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 November 2020","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 January 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":"24"}}