{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,26]],"date-time":"2026-09-26T02:49:05Z","timestamp":1790390945535,"version":"4.1.0"},"reference-count":39,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,3,7]],"date-time":"2024-03-07T00:00:00Z","timestamp":1709769600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,3,7]],"date-time":"2024-03-07T00:00:00Z","timestamp":1709769600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"Hainan Provincial Natural Science Foundation of China","award":["621QN246"],"award-info":[{"award-number":["621QN246"]}]},{"name":"Research Cultivation Foundation of Hainan Medical University","award":["HYPY2020039"],"award-info":[{"award-number":["HYPY2020039"]}]},{"name":"the Researchers Supporting Program of King Saud University","award":["RSPD2024R1052"],"award-info":[{"award-number":["RSPD2024R1052"]}]},{"name":"the Researchers Supporting Program of King Saud University","award":["RSPD2024R1052"],"award-info":[{"award-number":["RSPD2024R1052"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Cloud Comp"],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>In this study, we present a sophisticated multi-label deep learning framework for the prediction of RNA-RBP (RNA-binding protein) interactions, a critical aspect in understanding RNA functionality modulation and its implications in disease pathogenesis. Our approach leverages machine learning to develop a rapid and cost-efficient predictive model for these interactions. The proposed model captures the complex characteristics of RNA and recognizes corresponding RBPs through its dual-module architecture. The first module employs convolutional neural networks (CNNs) for intricate feature extraction from RNA sequences, enabling the model to discern nuanced patterns and attributes. The second module is a multi-view multi-label classification system incorporating a feature attention mechanism. The second module is a multi-view multi-label classification system that utilizes a feature attention mechanism. This mechanism is designed to intricately analyze and distinguish between common and unique deep features derived from the diverse RNA characteristics. To evaluate the model's efficacy, extensive experiments were conducted on a comprehensive RNA-RBP interaction dataset. The results emphasize substantial improvements in the model's ability to predict RNA-RBP interactions compared to existing methodologies. This advancement emphasizes the model's potential in contributing to the understanding of RNA-mediated biological processes and disease etiology.<\/jats:p>","DOI":"10.1186\/s13677-024-00612-0","type":"journal-article","created":{"date-parts":[[2024,3,7]],"date-time":"2024-03-07T07:02:34Z","timestamp":1709794954000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["RNA-RBP interactions recognition using multi-label learning and feature attention allocation"],"prefix":"10.1186","volume":"13","author":[{"given":"Huirui","family":"Han","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bandeh Ali","family":"Talpur","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Limei","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bilal","family":"Ahmed","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nadia","family":"Sarhan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Emad Mahrous","family":"Awwad","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,3,7]]},"reference":[{"key":"612_CR1","doi-asserted-by":"publisher","first-page":"67","DOI":"10.3389\/fmolb.2017.00067","volume":"4","author":"Handshakes and Fights","year":"2017","unstructured":"Handshakes and Fights (2017) The regulatory interplay of RNA-binding proteins. Front Mol Biosci 4:67","journal-title":"Front Mol Biosci"},{"issue":"9","key":"612_CR2","doi-asserted-by":"publisher","first-page":"4864","DOI":"10.1093\/nar\/gkab258","volume":"49","author":"K Asada","year":"2021","unstructured":"Asada K, Sakaue F, Nagata T, Zhang J, YoshidaTK Abe A, Yokota T (2021) Short DNA\/RNA heteroduplex oligonucleotide interacting proteins are key regulators of target gene silencing. Nucleic Acids Res 49(9):4864\u20134876","journal-title":"Nucleic Acids Res"},{"issue":"16","key":"612_CR3","doi-asserted-by":"publisher","first-page":"9408","DOI":"10.3390\/ijms23169408","volume":"23","author":"E Ilaslan","year":"2022","unstructured":"Ilaslan E, Sajek MP, Jaruzelska J, KuszZamelczyk K (2022) Emerging Roles of NANOS RNA-Binding Proteins in Cancer. Int J Mol Sci 23(16):9408\u20139408","journal-title":"Int J Mol Sci"},{"issue":"5","key":"612_CR4","doi-asserted-by":"publisher","first-page":"2598","DOI":"10.3390\/ijms22052598","volume":"22","author":"W Kim","year":"2021","unstructured":"Kim W, Kim D, Lee K (2021) RNA-binding proteins and the complex pathophysiology of ALS. Int J Mol Sci 22(5):2598\u20132598","journal-title":"Int J Mol Sci"},{"issue":"1\u20132","key":"612_CR5","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1007\/s11010-016-2936-2","volume":"429","author":"C Noyon","year":"2017","unstructured":"Noyon C, Roumegu\u00e8re T, Delporte C, Dufour D, Cortese M, Desmet J, Van AP (2017) The presence of modified nucleosides in extracellular fluids leads to the specific incorporation of 5-chlorocytidine into RNA and modulates the transcription and translation. Mol Cell Biochem 429(1\u20132):59\u201371","journal-title":"Mol Cell Biochem"},{"issue":"1","key":"612_CR6","doi-asserted-by":"publisher","first-page":"5868","DOI":"10.1038\/s41467-023-41284-9","volume":"14","author":"J Kristofich","year":"2023","unstructured":"Kristofich J, Nicchitta C (2023) Signal-noise metrics for RNA binding protein identification reveal broad spectrum protein-RNA interaction frequencies and dynamics. Nat Commun 14(1):5868\u20135868","journal-title":"Nat Commun"},{"key":"612_CR7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.jtbi.2016.04.025","volume":"402","author":"A Mohammad","year":"2016","unstructured":"Mohammad A, Javad Z, Reza R, Morteza E (2016) rpiCOOL: a tool for In Silico RNA\u2013protein interaction detection using random forest. J Theor Biol 402:1\u20138","journal-title":"J Theor Biol"},{"issue":"1","key":"612_CR8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12864-018-4889-1","volume":"19","author":"X Pan","year":"2018","unstructured":"Pan X, Rijnbeek P, Yan J, Shen H (2018) Prediction of RNA-protein sequence and structure binding preferences using deep convolutional and recurrent neural networks. BMC Genomics 19(1):1\u201311","journal-title":"BMC Genomics"},{"issue":"6","key":"612_CR9","first-page":"3741","volume":"14","author":"F Zheng","year":"2022","unstructured":"Zheng F, Chen G, Deng H (2022) Identifying the focuses of hereditary gingival fibromatosis with bioinformatics strategies. Am J Transl Res 14(6):3741\u20133749","journal-title":"Am J Transl Res"},{"key":"612_CR10","doi-asserted-by":"publisher","first-page":"814073","DOI":"10.3389\/fgene.2021.814073","volume":"12","author":"G Zhao","year":"2022","unstructured":"Zhao G, Li P, Qiao X, Han X, Liu ZP (2022) Predicting lncRNA-Protein Interactions by Heterogenous Network Embedding. Front Genet 12:814073","journal-title":"Front Genet"},{"issue":"3","key":"612_CR11","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 (2015) RPI-Pred: predicting ncRNA-protein interaction using sequence and structural information. Nucleic Acids Res 43(3):1370\u20139","journal-title":"Nucleic Acids Res"},{"issue":"23","key":"612_CR12","first-page":"3627","volume":"32","author":"C Gianluca","year":"2016","unstructured":"Gianluca C, Toma T, Fabrizio C, Paolo F, Andrea P (2016) RNAcommender: genome-wide recommendation of RNA-protein interactions. Bioinformatics (Oxford, England) 32(23):3627\u20133634","journal-title":"Bioinformatics (Oxford, England)"},{"issue":"1","key":"612_CR13","doi-asserted-by":"publisher","first-page":"213","DOI":"10.1007\/s11432-018-9558-2","volume":"62","author":"X Pan","year":"2019","unstructured":"Pan X, Fan Y, Jia J, Shen H (2019) Identifying RNA- binding proteins using multi- label deep learning. Sci China Inform Sci 62(1):213\u2013215","journal-title":"Sci China Inform Sci"},{"key":"612_CR14","doi-asserted-by":"publisher","first-page":"14588","DOI":"10.1109\/ACCESS.2019.2961260","volume":"8","author":"Z Li","year":"2020","unstructured":"Li Z, Zhu J, Xu X, Yao Y (2020) RDense: A protein-RNA binding prediction model based on bidirectional recurrent neural network and densely connected convolutional networks. IEEE Access 8:14588\u201314605","journal-title":"IEEE Access"},{"key":"612_CR15","doi-asserted-by":"crossref","unstructured":"Zhang Y, Jiang Y, Zhang Q, Liu D (2023) Multi-label learning based on instance correlation and feature redundancy. Pattern Recognition Letters 176:123\u2013130","DOI":"10.1016\/j.patrec.2023.10.029"},{"issue":"14","key":"612_CR16","doi-asserted-by":"publisher","first-page":"10407","DOI":"10.1007\/s00521-023-08241-5","volume":"35","author":"C Zhu","year":"2023","unstructured":"Zhu C, Zhao J, Hu S, Dong Y, Cao L, Zhou F, Zhou R (2023) A simple multiple-fold correlation-based multi-view multi-label learning. Neural Comput Appl 35(14):10407\u201310420","journal-title":"Neural Comput Appl"},{"key":"612_CR17","doi-asserted-by":"publisher","first-page":"100979","DOI":"10.1109\/ACCESS.2019.2930468","volume":"7","author":"J Huang","year":"2019","unstructured":"Huang J, Qu X, Li G, Qin F, Zheng X, Huang Q (2019) Multi-view multi-label learning with view-label-specific features. IEEE Access 7:100979\u2013100992","journal-title":"IEEE Access"},{"key":"612_CR18","doi-asserted-by":"publisher","first-page":"106841","DOI":"10.1016\/j.knosys.2021.106841","volume":"218","author":"D Zhao","year":"2021","unstructured":"Zhao D, Gao Q, Lu Y, Sun D, Cheng Y (2021) Consistency and diversity neural network multi-view multi-label learning. Knowl-Based Syst 218:106841","journal-title":"Knowl-Based Syst"},{"issue":"20","key":"612_CR19","doi-asserted-by":"publisher","first-page":"23655","DOI":"10.1007\/s10489-023-04699-x","volume":"53","author":"B Xiong","year":"2023","unstructured":"Xiong B, Chen H, Li T, Yang X (2023) Robust multi-view clustering in latent low-rank space with discrepancy induction. Appl Intell 53(20):23655\u201323674","journal-title":"Appl Intell"},{"issue":"24","key":"612_CR20","doi-asserted-by":"publisher","first-page":"38141","DOI":"10.1007\/s11042-022-14298-6","volume":"82","author":"G Liu","year":"2022","unstructured":"Liu G, Ge H, Su S, Wang S (2022) Low-rank tensor multi-view subspace clustering via cooperative regularization. Multimed Tools Appl 82(24):38141\u201338164","journal-title":"Multimed Tools Appl"},{"key":"612_CR21","doi-asserted-by":"crossref","unstructured":"A. Gretton, O. Bousquet, A. Smola, B. Scholkopf, Measuring statistical dependence with Hilbert-Schmidt norms, in: International conference on algorithmic learning theory, Springer, Berlin, Heidelberg, 2005, pp. 63\u201377.","DOI":"10.1007\/11564089_7"},{"key":"612_CR22","doi-asserted-by":"crossref","unstructured":"Zhao D, Gao Q, Lu Y, Sun D (2021) Two-step multi-view and multi-label learning with missing label via subspace learning. Applied Soft Computing(prepublish), 107120","DOI":"10.1016\/j.asoc.2021.107120"},{"key":"612_CR23","doi-asserted-by":"crossref","unstructured":"Cheng Y, Li Q, Wang Y, Zheng W (2022) Multi-view multi-label learning with view feature attention allocation. Neurocomputing 501:857\u2013874","DOI":"10.1016\/j.neucom.2022.06.068"},{"key":"612_CR24","doi-asserted-by":"publisher","first-page":"e27738","DOI":"10.4161\/trla.27738","volume":"2","author":"E Dassi","year":"2014","unstructured":"Dassi E, Re A, Leo S (2014) AURA 2: empowering discovery of post-transcriptional networks. Translation 2:e27738","journal-title":"Translation"},{"issue":"1","key":"612_CR25","doi-asserted-by":"publisher","first-page":"14","DOI":"10.3390\/genes12010014","volume":"12","author":"R Lorenz","year":"2020","unstructured":"Lorenz R, Stadler P (2020) RNA secondary structures with limited base pair span: exact backtracking and an application. Genes 12(1):14","journal-title":"Genes"},{"key":"612_CR26","doi-asserted-by":"publisher","DOI":"10.1101\/2020.08.17.254896","author":"Y Cao","year":"2021","unstructured":"Cao Y, Fu L (2021) Wu J UFold: fast and accurate RNA secondary structure prediction with deep learning. Nucleic Acids Res. https:\/\/doi.org\/10.1101\/2020.08.17.254896","journal-title":"Nucleic Acids Res"},{"issue":"1","key":"612_CR27","doi-asserted-by":"publisher","first-page":"13","DOI":"10.3390\/ncrna6010013","volume":"6","author":"K Ito","year":"2020","unstructured":"Ito K, Watanabe K, Kitagawa D (2020) The emerging role of ncRNAs and RNA-BINDING PROTEINS IN MITOTIC APPARATUS Formation. Non-Coding RNA 6(1):13\u201313","journal-title":"Non-Coding RNA"},{"key":"612_CR28","doi-asserted-by":"publisher","first-page":"107823","DOI":"10.1016\/j.jmgm.2020.107823","volume":"103","author":"M Taha","year":"2021","unstructured":"Taha M, El-Mageed H, Lee M (2021) DFT study of cyclic glycine-alanine dipeptide binding to gold nanoclusters. J Mol Graph Model 103:107823","journal-title":"J Mol Graph Model"},{"key":"612_CR29","doi-asserted-by":"publisher","first-page":"73","DOI":"10.1016\/j.bbrc.2020.12.027","volume":"535","author":"R Wakabayashi","year":"2021","unstructured":"Wakabayashi R, Hattori Y, Hosogi S, Toda Y, Ashihara E (2021) A novel dipeptide type inhibitor of the Wnt\/\u03b2-catenin pathway suppresses proliferation of acute myelogenous leukemia cells- Science Direct. Biochem Biophys Res Commun 535:73\u201379","journal-title":"Biochem Biophys Res Commun"},{"issue":"2","key":"612_CR30","doi-asserted-by":"publisher","first-page":"186","DOI":"10.1007\/s12539-015-0124-9","volume":"8","author":"P Feng","year":"2016","unstructured":"Feng P, Che W, Lin H (2016) Identifying Antioxidant Proteins by Using Optimal Dipeptide Compositions. Interdiscip Sci Comput Life Sci 8(2):186\u2013191","journal-title":"Interdiscip Sci Comput Life Sci"},{"key":"612_CR31","doi-asserted-by":"crossref","unstructured":"Hayate T ,Kana M ,Tohru H (2023) Trp-Tyr is a dipeptide structure that potently stimulates GLP-1 secretion in a murine enteroendocrine cell model, identified by comprehensive analysis. Biochem Biophys Res Commun 661:28\u201333","DOI":"10.1016\/j.bbrc.2023.04.042"},{"issue":"1","key":"612_CR32","doi-asserted-by":"publisher","first-page":"4719","DOI":"10.1038\/s41598-022-07303-3","volume":"12","author":"T Reza","year":"2022","unstructured":"Reza T, Rasouli F, Mahdi S, Ali M (2022) Convenient synthesis of dipeptide structures in solution phase assisted by a thioaza functionalized magnetic nanocatalyst. Sci Rep 12(1):4719\u20134719","journal-title":"Sci Rep"},{"issue":"1","key":"612_CR33","doi-asserted-by":"publisher","first-page":"7974","DOI":"10.1038\/s41467-023-43010-x","volume":"14","author":"K Liu","year":"2023","unstructured":"Liu K, vYe Y, Li S, Tang H (2023) Accurate de novo peptide sequencing using fully convolutional neural networks. Nat Commun 14(1):7974\u20137974","journal-title":"Nat Commun"},{"issue":"3","key":"612_CR34","doi-asserted-by":"publisher","first-page":"519","DOI":"10.1007\/s00521-019-04160-6","volume":"32","author":"A Baldominos","year":"2020","unstructured":"Baldominos A, Saez Y, Isasi P (2020) On the automated, evolutionary design of neural networks: past, present, and future. Neural Comput Appl 32(3):519\u2013545","journal-title":"Neural Comput Appl"},{"issue":"11","key":"612_CR35","doi-asserted-by":"publisher","first-page":"1084","DOI":"10.3390\/biology10111084","volume":"10","author":"Y Yan","year":"2021","unstructured":"Yan Y, Yao X, Wang S, Zhang Y (2021) A survey of computer-aided tumor diagnosis based on convolutional neural network. Biology 10(11):1084\u20131084","journal-title":"Biology"},{"key":"612_CR36","doi-asserted-by":"crossref","unstructured":"Wu X, Chen Q G, Hu Y, Wang D B, Chang X D, Wang X B, Zhang M L (2019) Multi-View Multi-Label Learning with View-Specific Information Extraction, in: IJCAI. 3884-3890","DOI":"10.24963\/ijcai.2019\/539"},{"key":"612_CR37","unstructured":"Ba JL, Kiros JR, Hinton GE (2016) Layer normalization. arXiv preprint arXiv:1607.06450"},{"issue":"6","key":"612_CR38","first-page":"743","volume":"12","author":"H Ahmed","year":"2013","unstructured":"Ahmed H, Joseph B (2013) Estimation of weighted log partial area under the ROC curve and its application to MicroRNA expression data. Stat Appl Genet Mol Biol 12(6):743\u201355","journal-title":"Stat Appl Genet Mol Biol"},{"key":"612_CR39","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1412.6980","author":"D Kingma","year":"2014","unstructured":"Kingma D (2014) Ba J Adam: A Method for Stochastic Optimization. Comput Sci. https:\/\/doi.org\/10.48550\/arXiv.1412.6980","journal-title":"Comput Sci"}],"container-title":["Journal of Cloud Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13677-024-00612-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s13677-024-00612-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13677-024-00612-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,7]],"date-time":"2024-03-07T07:07:47Z","timestamp":1709795267000},"score":1,"resource":{"primary":{"URL":"https:\/\/journalofcloudcomputing.springeropen.com\/articles\/10.1186\/s13677-024-00612-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,3,7]]},"references-count":39,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["612"],"URL":"https:\/\/doi.org\/10.1186\/s13677-024-00612-0","relation":{},"ISSN":["2192-113X"],"issn-type":[{"value":"2192-113X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,3,7]]},"assertion":[{"value":"31 December 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 February 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 March 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This article does not contain any studies with human participants or animals performed by any of the authors.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"The authors declare no competing interests.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"54"}}