{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,30]],"date-time":"2025-09-30T00:12:28Z","timestamp":1759191148556,"version":"3.44.0"},"reference-count":38,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,9,29]],"date-time":"2025-09-29T00:00:00Z","timestamp":1759104000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,9,29]],"date-time":"2025-09-29T00:00:00Z","timestamp":1759104000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["No. 32200546"],"award-info":[{"award-number":["No. 32200546"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100003787","name":"Hebei Natural Science Foundation","doi-asserted-by":"crossref","award":["No. C2024202003"],"award-info":[{"award-number":["No. C2024202003"]}],"id":[{"id":"10.13039\/501100003787","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BioData Mining"],"DOI":"10.1186\/s13040-025-00481-6","type":"journal-article","created":{"date-parts":[[2025,9,29]],"date-time":"2025-09-29T14:54:42Z","timestamp":1759157682000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["MoRFs_TransFuse: a MoRFs predictor based on multimodal feature fusion and the lightweight Transformer network"],"prefix":"10.1186","volume":"18","author":[{"given":"Lele","family":"Zhang","sequence":"first","affiliation":[]},{"given":"Hao","family":"He","sequence":"additional","affiliation":[]},{"given":"Xuesen","family":"Shi","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,9,29]]},"reference":[{"issue":"3","key":"481_CR1","doi-asserted-by":"publisher","first-page":"187","DOI":"10.1038\/s41580-023-00673-0","volume":"25","author":"AS Holehouse","year":"2024","unstructured":"Holehouse AS, Kragelund BB. The molecular basis for cellular function of intrinsically disordered protein regions. Nat Rev Mol Cell Biol. 2024;25(3):187\u2013211.","journal-title":"Nat Rev Mol Cell Biol"},{"key":"481_CR2","doi-asserted-by":"publisher","first-page":"1487","DOI":"10.1016\/j.csbj.2023.02.018","volume":"21","author":"S Basu","year":"2023","unstructured":"Basu S, Kihara D, Kurgan L. Computational prediction of disordered binding regions. Comput Struct Biotechnol J. 2023;21:1487\u201397.","journal-title":"Comput Struct Biotechnol J"},{"key":"481_CR3","doi-asserted-by":"publisher","unstructured":"McConnell BS, Parker MW. Protein intrinsically disordered regions have a non-random, modular architecture. Bioinformatics. 2023. https:\/\/doi.org\/10.1093\/bioinformatics\/btad732.","DOI":"10.1093\/bioinformatics\/btad732"},{"key":"481_CR4","doi-asserted-by":"publisher","unstructured":"Peng Z, Li Z, Meng Q, Zhao B, Kurgan L. CLIP: accurate prediction of disordered linear interacting peptides from protein sequences using co-evolutionary information. Brief Bioinform. 2023. https:\/\/doi.org\/10.1093\/bib\/bbac502.","DOI":"10.1093\/bib\/bbac502"},{"key":"481_CR5","doi-asserted-by":"crossref","unstructured":"Kibar G, Vingron M. Prediction of protein-protein interactions using sequences of intrinsically disordered regions. Proteins. 2023;91(7):980\u201390.","DOI":"10.1002\/prot.26486"},{"issue":"1","key":"481_CR6","doi-asserted-by":"publisher","DOI":"10.1186\/s13040-021-00275-6","volume":"14","author":"H He","year":"2021","unstructured":"He H, Zhou Y, Chi Y, He J. Prediction of MoRFs based on sequence properties and convolutional neural networks. Biodata Min. 2021;14(1):39.","journal-title":"Biodata Min"},{"issue":"5","key":"481_CR7","doi-asserted-by":"publisher","first-page":"1252","DOI":"10.1093\/bioinformatics\/btab810","volume":"38","author":"YJ Tang","year":"2022","unstructured":"Tang YJ, Pang YH, Liu B. DeepIDP-2L: protein intrinsically disordered region prediction by combining convolutional attention network and hierarchical attention network. Bioinformatics. 2022;38(5):1252\u201360.","journal-title":"Bioinformatics"},{"issue":"13","key":"481_CR8","doi-asserted-by":"publisher","DOI":"10.1186\/s12859-018-2396-7","volume":"19","author":"R Sharma","year":"2019","unstructured":"Sharma R, Sharma A, Patil A, Tsunoda T. Discovering MoRFs by trisecting intrinsically disordered protein sequence into terminals and middle regions. BMC Bioinform. 2019;19(13):378.","journal-title":"BMC Bioinform"},{"key":"481_CR9","doi-asserted-by":"publisher","unstructured":"Zhang F, Zhao B, Shi W, Li M, Kurgan L. DeepDISOBind: accurate prediction of RNA-, DNA- and protein-binding intrinsically disordered residues with deep multi-task learning. Brief Bioinform. 2022. https:\/\/doi.org\/10.1093\/bib\/bbab521.","DOI":"10.1093\/bib\/bbab521"},{"key":"481_CR10","doi-asserted-by":"crossref","unstructured":"Hanson J, Litfin T, Paliwal K, Zhou Y. Identifying molecular recognition features in intrinsically disordered regions of proteins by transfer learning. Bioinformatics. 2020;36(4):1107\u201313.","DOI":"10.1093\/bioinformatics\/btz691"},{"key":"481_CR11","doi-asserted-by":"crossref","unstructured":"Katuwawala A, Peng Z, Yang J, Kurgan L. Computational prediction of MoRFs, short disorder-to-order transitioning protein binding regions. Comput Struct Biotechnol J. 2019;17:454\u201362.","DOI":"10.1016\/j.csbj.2019.03.013"},{"key":"481_CR12","doi-asserted-by":"crossref","unstructured":"Oldfield CJ, Cheng Y, Cortese MS, Romero P, Uversky VN, Dunker AK. Coupled folding and binding with alpha-helix-forming molecular recognition elements. Biochemistry. 2005;44(37):12454\u201370.","DOI":"10.1021\/bi050736e"},{"issue":"12","key":"481_CR13","doi-asserted-by":"publisher","first-page":"i75","DOI":"10.1093\/bioinformatics\/bts209","volume":"28","author":"FM Disfani","year":"2012","unstructured":"Disfani FM, Hsu WL, Mizianty MJ, Oldfield CJ, Xue B, Dunker AK, et al. MoRFpred, a computational tool for sequence-based prediction and characterization of short disorder-to-order transitioning binding regions in proteins. Bioinformatics. 2012;28(12):i75\u201383.","journal-title":"Bioinformatics"},{"issue":"16","key":"481_CR14","doi-asserted-by":"publisher","first-page":"1798","DOI":"10.1093\/bioinformatics\/btn326","volume":"24","author":"LJ McGuffin","year":"2008","unstructured":"McGuffin LJ. Intrinsic disorder prediction from the analysis of multiple protein fold recognition models. Bioinformatics. 2008;24(16):1798\u2013804.","journal-title":"Bioinformatics"},{"issue":"7","key":"481_CR15","doi-asserted-by":"publisher","first-page":"891","DOI":"10.1093\/bioinformatics\/btl032","volume":"22","author":"A Schlessinger","year":"2006","unstructured":"Schlessinger A, Yachdav G, Rost B. Profbval: predict flexible and rigid residues in proteins. Bioinformatics. 2006;22(7):891\u20133.","journal-title":"Bioinformatics"},{"issue":"2","key":"481_CR16","doi-asserted-by":"publisher","first-page":"121","DOI":"10.1023\/A:1009715923555","volume":"2","author":"CJC Burges","year":"1998","unstructured":"Burges CJC. A Tutorial on Support Vector Machines for Pattern Recognition. Data Min Knowl Discov. 1998;2(2):121\u201367.","journal-title":"Data Min Knowl Discov."},{"issue":"Database issue","key":"481_CR17","first-page":"D202","volume":"36","author":"S Kawashima","year":"2008","unstructured":"Kawashima S, Pokarowski P, Pokarowska M, Kolinski A, Katayama T, Kanehisa M. AAindex: amino acid index database, progress report 2008. Nucleic Acids Res. 2008;36(Database issue):D202-5.","journal-title":"Nucleic Acids Res"},{"issue":"W1","key":"481_CR18","doi-asserted-by":"publisher","first-page":"W488","DOI":"10.1093\/nar\/gkw409","volume":"44","author":"N Malhis","year":"2016","unstructured":"Malhis N, Jacobson M, Gsponer J. MoRFchibi system: software tools for the identification of MoRFs in protein sequences. Nucleic Acids Res. 2016;44(W1):W488-93.","journal-title":"Nucleic Acids Res"},{"issue":"10","key":"481_CR19","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0141603","volume":"10","author":"N Malhis","year":"2015","unstructured":"Malhis N, Wong ET, Nassar R, Gsponer J. Computational identification of MoRFs in protein sequences using hierarchical application of Bayes rule. PLoS One. 2015;10(10):e0141603.","journal-title":"PLoS One"},{"issue":"4","key":"481_CR20","doi-asserted-by":"publisher","first-page":"503","DOI":"10.1093\/bioinformatics\/btr682","volume":"28","author":"I Walsh","year":"2012","unstructured":"Walsh I, Martin AJ, Di Domenico T, Tosatto SC. ESpritz: accurate and fast prediction of protein disorder. Bioinformatics. 2012;28(4):503\u20139.","journal-title":"Bioinformatics."},{"issue":"11","key":"481_CR21","doi-asserted-by":"publisher","first-page":"1850","DOI":"10.1093\/bioinformatics\/bty032","volume":"34","author":"R Sharma","year":"2018","unstructured":"Sharma R, Raicar G, Tsunoda T, Patil A, Sharma A. OPAL: prediction of MoRF regions in intrinsically disordered protein sequences. Bioinformatics. 2018;34(11):1850\u20138.","journal-title":"Bioinformatics"},{"issue":"8","key":"481_CR22","doi-asserted-by":"publisher","first-page":"2102","DOI":"10.1093\/bioinformatics\/btac020","volume":"38","author":"N Brandes","year":"2022","unstructured":"Brandes N, Ofer D, Peleg Y, Rappoport N, Linial M. ProteinBERT: a universal deep-learning model of protein sequence and function. Bioinformatics. 2022;38(8):2102\u201310.","journal-title":"Bioinformatics"},{"issue":"6637","key":"481_CR23","doi-asserted-by":"publisher","first-page":"1123","DOI":"10.1126\/science.ade2574","volume":"379","author":"Z Lin","year":"2023","unstructured":"Lin Z, Akin H, Rao R, Hie B, Zhu Z, Lu W, et al. Evolutionary-scale prediction of atomic-level protein structure with a language model. Science. 2023;379(6637):1123\u201330.","journal-title":"Science"},{"issue":"1","key":"481_CR24","doi-asserted-by":"publisher","DOI":"10.1186\/s12859-019-3111-z","volume":"20","author":"H He","year":"2019","unstructured":"He H, Zhao J, Sun G. Computational prediction of MoRFs based on protein sequences and minimax probability machine. BMC Bioinformatics. 2019;20(1):529.","journal-title":"BMC Bioinformatics"},{"issue":"13","key":"481_CR25","doi-asserted-by":"publisher","first-page":"3701","DOI":"10.1093\/nar\/gkg519","volume":"31","author":"R Linding","year":"2003","unstructured":"Linding R, Russell RB, Neduva V, Gibson TJ. Globplot: exploring protein sequences for globularity and disorder. Nucleic Acids Res. 2003;31(13):3701\u20138.","journal-title":"Nucleic Acids Res"},{"key":"481_CR26","doi-asserted-by":"publisher","DOI":"10.1186\/1471-2105-7-319","volume":"7","author":"CT Su","year":"2006","unstructured":"Su CT, Chen CY, Ou YY. Protein disorder prediction by condensed PSSM considering propensity for order or disorder. BMC Bioinformatics. 2006;7:319.","journal-title":"BMC Bioinformatics"},{"issue":"Database issue","key":"481_CR27","doi-asserted-by":"publisher","first-page":"D32","DOI":"10.1093\/nar\/gkn721","volume":"37","author":"KD Pruitt","year":"2009","unstructured":"Pruitt KD, Tatusova T, Klimke W, Maglott DR. Ncbi reference sequences: current status, policy and new initiatives. Nucleic Acids Res. 2009;37(Database issue):D32-6.","journal-title":"Nucleic Acids Res"},{"issue":"11","key":"481_CR28","doi-asserted-by":"publisher","first-page":"20501","DOI":"10.1002\/jcp.28650","volume":"234","author":"B He","year":"2019","unstructured":"He B, Ji T, Zhang H, Zhu Y, Shu R, Zhao W, et al. MRI-based radiomics signature for tumor grading of rectal carcinoma using random forest model. J Cell Physiol. 2019;234(11):20501\u20139.","journal-title":"J Cell Physiol"},{"key":"481_CR29","doi-asserted-by":"publisher","DOI":"10.1177\/11769351231167992","volume":"22","author":"C L","year":"2023","unstructured":"L C, P S, Ah Kashyap, A Rahaman, S Niranjan, V Niranjan. Novel biomarker prediction for lung cancer using random forest classifiers. Cancer Inform. 2023;22:11769351231167992.","journal-title":"Cancer Inform"},{"key":"481_CR30","doi-asserted-by":"publisher","DOI":"10.3389\/fgene.2022.885627","volume":"13","author":"K Lin","year":"2022","unstructured":"Lin K, Quan X, Jin C, Shi Z, Yang J. An interpretable double-scale attention model for enzyme protein class prediction based on transformer encoders and multi-scale convolutions. Front Genet. 2022;13:885627.","journal-title":"Front Genet"},{"key":"481_CR31","doi-asserted-by":"publisher","unstructured":"Zhou Y, Wang X, Yao L, Zhu M. LDAformer: predicting lncRNA-disease associations based on topological feature extraction and transformer encoder. Brief Bioinform. 2022. https:\/\/doi.org\/10.1093\/bib\/bbac370.","DOI":"10.1093\/bib\/bbac370"},{"issue":"Supplement_1","key":"481_CR32","doi-asserted-by":"publisher","first-page":"i217","DOI":"10.1093\/bioinformatics\/btaf178","volume":"41","author":"M Hoang","year":"2025","unstructured":"Hoang M, Singh M. Locality-aware pooling enhances protein language model performance across varied applications. Bioinformatics. 2025;41(Supplement_1):i217-26.","journal-title":"Bioinformatics"},{"key":"481_CR33","doi-asserted-by":"crossref","unstructured":"Jin C, Shi Z, Kang C, Lin K, Zhang H. Tlcrys: transfer learning based method for protein crystallization prediction. Int J Mol Sci. 2022;23(2):972.","DOI":"10.3390\/ijms23020972"},{"key":"481_CR34","doi-asserted-by":"publisher","unstructured":"Yuan L, Sun S, Jiang Y, Zhang Q, Ye L, Zheng C-H, et al. ScRGCL: a cell type annotation method for single-cell RNA-seq data using residual graph convolutional neural network with contrastive learning. Brief Bioinform. 2024. https:\/\/doi.org\/10.1093\/bib\/bbae662.","DOI":"10.1093\/bib\/bbae662"},{"key":"481_CR35","doi-asserted-by":"crossref","unstructured":"Bao W, Liu Y, Chen B. Oral_voting_transfer: classification of oral microorganisms\u2019 function proteins with voting transfer model. Front Microbiol. 2023;14:1277121.","DOI":"10.3389\/fmicb.2023.1277121"},{"key":"481_CR36","doi-asserted-by":"crossref","unstructured":"Chen B, Li N, Bao W. CLPr_in_ML: cleft lip and palate reconstructed features with machine learning. Curr Bioinform. 2025;20(2):179\u201393.","DOI":"10.2174\/0115748936330499240909082529"},{"key":"481_CR37","doi-asserted-by":"crossref","unstructured":"Yuan L, Xu Z, Meng B, Ye L. scAMZI: attention-based deep autoencoder with zero-inflated layer for clustering scRNA-seq data. BMC Genomics. 2025;26(1):350.","DOI":"10.1186\/s12864-025-11511-2"},{"key":"481_CR38","doi-asserted-by":"publisher","unstructured":"Yuan L, Zhao L, Jiang Y, Shen Z, Zhang Q, Zhang M, et al. scMGATGRN: a multiview graph attention network-based method for inferring gene regulatory networks from single-cell transcriptomic data. Brief Bioinform. 2024. https:\/\/doi.org\/10.1093\/bib\/bbae526.","DOI":"10.1093\/bib\/bbae526"}],"container-title":["BioData Mining"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13040-025-00481-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s13040-025-00481-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13040-025-00481-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,29]],"date-time":"2025-09-29T14:54:45Z","timestamp":1759157685000},"score":1,"resource":{"primary":{"URL":"https:\/\/biodatamining.biomedcentral.com\/articles\/10.1186\/s13040-025-00481-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,29]]},"references-count":38,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["481"],"URL":"https:\/\/doi.org\/10.1186\/s13040-025-00481-6","relation":{},"ISSN":["1756-0381"],"issn-type":[{"value":"1756-0381","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,29]]},"assertion":[{"value":"22 July 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 August 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 September 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"65"}}