{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T01:10:00Z","timestamp":1773796200249,"version":"3.50.1"},"reference-count":60,"publisher":"Oxford University Press (OUP)","issue":"3","license":[{"start":{"date-parts":[[2024,4,16]],"date-time":"2024-04-16T00:00:00Z","timestamp":1713225600000},"content-version":"vor","delay-in-days":20,"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":["32270786"],"award-info":[{"award-number":["32270786"]}],"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":["62172076"],"award-info":[{"award-number":["62172076"]}],"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":["U22A2038"],"award-info":[{"award-number":["U22A2038"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Zhejiang Provincial Natural Science Foundation of China","award":["LY23F020003"],"award-info":[{"award-number":["LY23F020003"]}]},{"name":"Municipal Government of Quzhou","award":["2023D036"],"award-info":[{"award-number":["2023D036"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,3,27]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Pseudouridine is an RNA modification that is widely distributed in both prokaryotes and eukaryotes, and plays a critical role in numerous biological activities. Despite its importance, the precise identification of pseudouridine sites through experimental approaches poses significant challenges, requiring substantial time and resources.Therefore, there is a growing need for computational techniques that can reliably and quickly identify pseudouridine sites from vast amounts of RNA sequencing data. In this study, we propose fuzzy kernel evidence Random Forest (FKeERF) to identify pseudouridine sites. This method is called PseU-FKeERF, which demonstrates high accuracy in identifying pseudouridine sites from RNA sequencing data. The PseU-FKeERF model selected four RNA feature coding schemes with relatively good performance for feature combination, and then input them into the newly proposed FKeERF method for category prediction. FKeERF not only uses fuzzy logic to expand the original feature space, but also combines kernel methods that are easy to interpret in general for category prediction. Both cross-validation tests and independent tests on benchmark datasets have shown that PseU-FKeERF has better predictive performance than several state-of-the-art methods. This new method not only improves the accuracy of pseudouridine site identification, but also provides a certain reference for disease control and related drug development in the future.<\/jats:p>","DOI":"10.1093\/bib\/bbae169","type":"journal-article","created":{"date-parts":[[2024,4,16]],"date-time":"2024-04-16T01:50:21Z","timestamp":1713232221000},"source":"Crossref","is-referenced-by-count":2,"title":["Fuzzy kernel evidence Random Forest for identifying pseudouridine sites"],"prefix":"10.1093","volume":"25","author":[{"given":"Mingshuai","family":"Chen","sequence":"first","affiliation":[{"name":"Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China , Chengdu 611731 , China"},{"name":"Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China , Quzhou 324003 , China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mingai","family":"Sun","sequence":"additional","affiliation":[{"name":"Beidahuang Industry Group General Hospital , Harbin 150001 , China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xi","family":"Su","sequence":"additional","affiliation":[{"name":"Foshan Women and Children Hospital , Foshan 528000 , China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Prayag","family":"Tiwari","sequence":"additional","affiliation":[{"name":"School of Information Technology, Halmstad University , Sweden"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yijie","family":"Ding","sequence":"additional","affiliation":[{"name":"Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China , Quzhou 324003 , China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2024,4,15]]},"reference":[{"issue":"4","key":"2024041601501452900_ref1","doi-asserted-by":"crossref","first-page":"210","DOI":"10.1016\/j.tibs.2013.01.002","article-title":"RNA pseudouridylation: new insights into an old modification","volume":"38","author":"Ge","year":"2013","journal-title":"Trends Biochem Sci"},{"issue":"5","key":"2024041601501452900_ref2","doi-asserted-by":"crossref","first-page":"341","DOI":"10.1080\/152165400410182","article-title":"Pseudouridine in RNA: what, where, how, and why","volume":"49","author":"Charette","year":"2000","journal-title":"IUBMB Life"},{"issue":"2","key":"2024041601501452900_ref3","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1261\/rna.069112.118","article-title":"Gene2Vec: gene subsequence embedding for prediction of mammalian N-6-methyladenosine sites from mRNA","volume":"25","author":"Zou","year":"2019","journal-title":"RNA"},{"issue":"3","key":"2024041601501452900_ref4","doi-asserted-by":"crossref","first-page":"400","DOI":"10.1038\/s12276-020-0407-z","article-title":"The emerging role of RNA modifications in the regulation of mRNA stability","volume":"52","author":"Boo","year":"2020","journal-title":"Exp Mol Med"},{"issue":"4","key":"2024041601501452900_ref5","doi-asserted-by":"crossref","first-page":"966","DOI":"10.1016\/j.celrep.2014.07.004","article-title":"A Pseudouridine residue in the spliceosome core is part of the filamentous growth program in yeast","volume":"8","author":"Basak","year":"2014","journal-title":"Cell Rep"},{"issue":"4","key":"2024041601501452900_ref6","doi-asserted-by":"crossref","first-page":"660","DOI":"10.1016\/j.molcel.2011.09.017","article-title":"rRNA Pseudouridylation defects affect ribosomal ligand binding and translational fidelity from yeast to human cells","volume":"44","author":"Jack","year":"2011","journal-title":"Mol Cell"},{"issue":"1","key":"2024041601501452900_ref7","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13059-022-02780-1","article-title":"iDNA-ABF: multi-scale deep biological language learning model for the interpretable prediction of DNA methylations","volume":"23","author":"Jin","year":"2022","journal-title":"Genome Biol"},{"issue":"7525","key":"2024041601501452900_ref8","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1038\/nature13802","article-title":"Pseudouridine profiling reveals regulated mRNA pseudouridylation in yeast and human cells","volume":"515","author":"Carlile","year":"2014","journal-title":"Nature"},{"issue":"1","key":"2024041601501452900_ref9","doi-asserted-by":"crossref","first-page":"148","DOI":"10.1016\/j.cell.2014.08.028","article-title":"Transcriptome-wide mapping reveals widespread dynamic-regulated pseudouridylation of ncRNA and mRNA","volume":"159","author":"Schwartz","year":"2014","journal-title":"Cell"},{"issue":"22","key":"2024041601501452900_ref10","doi-asserted-by":"crossref","first-page":"2794","DOI":"10.1038\/onc.2011.449","article-title":"Small nucleolar RNA 42 acts as an oncogene in lung tumorigenesis","volume":"31","author":"Mei","year":"2012","journal-title":"Oncogene"},{"issue":"D1","key":"2024041601501452900_ref11","doi-asserted-by":"crossref","first-page":"D1123","DOI":"10.1093\/nar\/gkab957","article-title":"webTWAS: a resource for disease candidate susceptibility genes identified by transcriptome-wide association study","volume":"50","author":"Cao","year":"2022","journal-title":"Nucleic Acids Res"},{"issue":"3","key":"2024041601501452900_ref12","doi-asserted-by":"crossref","first-page":"398","DOI":"10.1093\/bioinformatics\/btx622","article-title":"Tumor origin detection with tissue-specific miRNA and DNA methylation markers","volume":"34","author":"Tang","year":"2018","journal-title":"Bioinformatics"},{"key":"2024041601501452900_ref13","doi-asserted-by":"crossref","DOI":"10.1093\/bioinformatics\/btad059","article-title":"Potent antibiotic design via guided search from antibacterial activity evaluations","volume":"39","author":"Chen","year":"2023","journal-title":"Bioinformatics"},{"issue":"4","key":"2024041601501452900_ref14","doi-asserted-by":"crossref","first-page":"1598","DOI":"10.1002\/alz.13016","article-title":"2023 Alzheimer\u2019s disease facts and figures","volume":"19","year":"2023","journal-title":"Alzheimers Dement"},{"issue":"1","key":"2024041601501452900_ref15","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1186\/s12916-020-01883-5","article-title":"rs1990622 variant associates with Alzheimer\u2019s disease and regulates TMEM106B expression in human brain tissues","volume":"19","author":"Hu","year":"2021","journal-title":"BMC Med"},{"issue":"11","key":"2024041601501452900_ref16","doi-asserted-by":"crossref","DOI":"10.1093\/brain\/awaa302","article-title":"rs34331204 regulates TSPAN13 expression and contributes to Alzheimer\u2019s disease with sex differences","volume":"143","author":"Hu","year":"2020","journal-title":"Brain"},{"issue":"10","key":"2024041601501452900_ref17","doi-asserted-by":"crossref","first-page":"2003","DOI":"10.1002\/alz.12687","article-title":"Mendelian randomization highlights causal association between genetically increased C-reactive protein levels and reduced Alzheimer\u2019s disease risk","volume":"18","author":"Hu","year":"2022","journal-title":"Alzheimers Dement"},{"issue":"10","key":"2024041601501452900_ref18","doi-asserted-by":"crossref","first-page":"4297","DOI":"10.1038\/s41380-022-01695-4","article-title":"Cognitive performance protects against Alzheimer\u2019s disease independently of educational attainment and intelligence","volume":"27","author":"Hu","year":"2022","journal-title":"Mol Psychiatry"},{"issue":"20","key":"2024041601501452900_ref19","doi-asserted-by":"crossref","first-page":"3362","DOI":"10.1093\/bioinformatics\/btv366","article-title":"PPUS: a web server to predict PUS-specific pseudouridine sites","volume":"31","author":"Li","year":"2015","journal-title":"Bioinformatics"},{"issue":"5","key":"2024041601501452900_ref20","doi-asserted-by":"crossref","first-page":"473","DOI":"10.2174\/1574893617666220404145517","article-title":"Distance-based support vector machine to predict DNA N6-methyladenine modification","volume":"17","author":"Zhang","year":"2022","journal-title":"Current Bioinformatics"},{"key":"2024041601501452900_ref21","article-title":"SBSM-pro: support bio-sequence machine for proteins","author":"Wang","year":"2023"},{"key":"2024041601501452900_ref22","first-page":"9","article-title":"iRNA-PseU: identifying RNA pseudouridine sites","volume":"5","author":"Chen","year":"2016","journal-title":"Mol Ther-Nucleic Acids"},{"key":"2024041601501452900_ref23","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1186\/s12859-018-2321-0","article-title":"PseUI: pseudouridine sites identification based on RNA sequence information","volume":"19","author":"He","year":"2018","journal-title":"BMC Bioinform"},{"key":"2024041601501452900_ref24","doi-asserted-by":"crossref","first-page":"463","DOI":"10.1016\/j.omtn.2019.03.010","article-title":"iPseU-CNN: identifying RNA pseudouridine sites using convolutional neural networks","volume":"16","author":"Tahir","year":"2019","journal-title":"Mol Ther-Nucleic Acids"},{"issue":"1","key":"2024041601501452900_ref25","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1007\/s00438-019-01600-9","article-title":"XG-PseU: an eXtreme gradient boosting based method for identifying pseudouridine sites","volume":"295","author":"Liu","year":"2020","journal-title":"Mol Genet Genomics"},{"key":"2024041601501452900_ref26","doi-asserted-by":"crossref","first-page":"79376","DOI":"10.1109\/ACCESS.2020.2989469","article-title":"EnsemPseU: identifying pseudouridine sites with an ensemble approach","volume":"8","author":"Bi","year":"2020","journal-title":"IEEE Access"},{"key":"2024041601501452900_ref27","doi-asserted-by":"crossref","first-page":"10","DOI":"10.3389\/fbioe.2020.00134","article-title":"RF-PseU: a random Forest predictor for RNA Pseudouridine sites","volume":"8","author":"Lv","year":"2020","journal-title":"Front Bioeng Biotechnol"},{"key":"2024041601501452900_ref28","doi-asserted-by":"crossref","first-page":"1877","DOI":"10.1016\/j.csbj.2020.07.010","article-title":"MU-PseUDeep: a deep learning method for prediction of pseudouridine sites","volume":"18","author":"Khan","year":"2020","journal-title":"Comput Struct Biotechnol J"},{"issue":"6","key":"2024041601501452900_ref29","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1093\/bib\/bbab245","article-title":"Porpoise: a new approach for accurate prediction of RNA pseudouridine sites","volume":"22","author":"Li","year":"2021","journal-title":"Brief Bioinform"},{"key":"2024041601501452900_ref30","doi-asserted-by":"crossref","first-page":"9","DOI":"10.3389\/fgene.2021.773882","article-title":"PseUdeep: RNA pseudouridine site identification with deep learning algorithm","volume":"12","author":"Zhuang","year":"2021","journal-title":"Front Genet"},{"issue":"3","key":"2024041601501452900_ref31","doi-asserted-by":"crossref","first-page":"1844","DOI":"10.3390\/cimb43030129","article-title":"A feature fusion predictor for RNA pseudouridine sites with particle swarm optimizer based feature selection and ensemble learning approach","volume":"43","author":"Wang","year":"2021","journal-title":"Curr Issues Mol Biol"},{"issue":"8","key":"2024041601501452900_ref32","doi-asserted-by":"crossref","first-page":"1200","DOI":"10.1109\/TNNLS.2013.2253617","article-title":"Knowledge-leverage-based TSK fuzzy system modeling","volume":"24","author":"Deng","year":"2013","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"2024041601501452900_ref33","doi-asserted-by":"crossref","article-title":"Upper and lower probabilities induced by a multivalued mapping","author":"Dempster","DOI":"10.1007\/978-3-540-44792-4_3"},{"key":"2024041601501452900_ref34","doi-asserted-by":"crossref","DOI":"10.1515\/9780691214696","volume-title":"A Mathematical Theory of Evidence","author":"Shafer","year":"1976"},{"issue":"2","key":"2024041601501452900_ref35","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1016\/S1566-2535(01)00026-4","article-title":"A new distance between two bodies of evidence","volume":"2","author":"Jousselme","year":"2001","journal-title":"Inf Fusion"},{"key":"2024041601501452900_ref36","first-page":"79","volume-title":"Conflict Management in Information Fusion with Belief Functions","author":"Martin","year":"2019"},{"issue":"3","key":"2024041601501452900_ref37","doi-asserted-by":"crossref","first-page":"1047","DOI":"10.1093\/bib\/bbz041","article-title":"iLearn: an integrated platform and meta-learner for feature engineering, machine-learning analysis and modeling of DNA, RNA and protein sequence data","volume":"21","author":"Chen","year":"2020","journal-title":"Brief Bioinform"},{"issue":"10","key":"2024041601501452900_ref38","doi-asserted-by":"crossref","first-page":"e60","DOI":"10.1093\/nar\/gkab122","article-title":"iLearnPlus: a comprehensive and automated machine-learning platform for nucleic acid and protein sequence analysis, prediction and visualization","volume":"49","author":"Chen","year":"2021","journal-title":"Nucleic Acids Res"},{"issue":"22","key":"2024041601501452900_ref39","doi-asserted-by":"crossref","first-page":"e129","DOI":"10.1093\/nar\/gkab829","article-title":"BioSeq-BLM: a platform for analyzing DNA, RNA, and protein sequences based on biological language models","volume":"49","author":"Li","year":"2021","journal-title":"Nucleic Acids Res"},{"issue":"1","key":"2024041601501452900_ref40","doi-asserted-by":"crossref","DOI":"10.1186\/s12915-023-01596-0","article-title":"m5U-SVM: identification of RNA 5-methyluridine modification sites based on multi-view features of physicochemical features and distributed representation","volume":"21","author":"Ao","year":"2023","journal-title":"BMC Biol"},{"key":"2024041601501452900_ref41","doi-asserted-by":"crossref","first-page":"1174","DOI":"10.1016\/j.ijbiomac.2022.11.299","article-title":"iRNA-ac4C: a novel computational method for effectively detecting N4-acetylcytidine sites in human mRNA","volume":"227","author":"Su","year":"2023","journal-title":"Int J Biol Macromol"},{"issue":"7","key":"2024041601501452900_ref42","doi-asserted-by":"crossref","first-page":"3017","DOI":"10.1093\/nar\/gkad055","article-title":"DeepBIO: an automated and interpretable deep-learning platform for high-throughput biological sequence prediction, functional annotation and visualization analysis","volume":"51","author":"Wang","year":"2023","journal-title":"Nucleic Acids Res"},{"key":"2024041601501452900_ref43","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1016\/j.ins.2023.01.149","article-title":"A deep multiple kernel learning-based higher-order fuzzy inference system for identifying DNA N4-methylcytosine sites","volume":"630","author":"Wang","year":"2023","journal-title":"Inform Sci"},{"key":"2024041601501452900_ref44","doi-asserted-by":"crossref","first-page":"120652","DOI":"10.1016\/j.eswa.2023.120652","article-title":"Evidential random forests","volume":"230","author":"Hoarau","year":"2023","journal-title":"Exp Syst Appl"},{"issue":"3","key":"2024041601501452900_ref45","doi-asserted-by":"crossref","first-page":"1485","DOI":"10.1109\/TIT.2016.2514489","article-title":"Random forests and kernel methods","volume":"62","author":"Scornet","year":"2016","journal-title":"IEEE Trans Inf Theory"},{"issue":"6","key":"2024041601501452900_ref46","doi-asserted-by":"crossref","first-page":"e1011214","DOI":"10.1371\/journal.pcbi.1011214","article-title":"BioSeq-diabolo: biological sequence similarity analysis using diabolo","volume":"19","author":"Li","year":"2023","journal-title":"PLoS Comput Biol"},{"issue":"D1","key":"2024041601501452900_ref47","doi-asserted-by":"crossref","first-page":"D259","DOI":"10.1093\/nar\/gkv1036","article-title":"RMBase: a resource for decoding the landscape of RNA modifications from high-throughput sequencing data","volume":"44","author":"Sun","year":"2016","journal-title":"Nucleic Acids Res"},{"issue":"17","key":"2024041601501452900_ref48","doi-asserted-by":"crossref","first-page":"14","DOI":"10.3390\/ijms20174175","article-title":"FKRR-MVSF: a fuzzy kernel ridge regression model for identifying DNA-binding proteins by multi-view sequence features via Chou\u2019s five-step rule","volume":"20","author":"Zou","year":"2019","journal-title":"Int J Mol Sci"},{"key":"2024041601501452900_ref49","doi-asserted-by":"crossref","first-page":"618","DOI":"10.1016\/j.neucom.2021.05.100","article-title":"Identification of drug-target interactions via multi-view graph regularized link propagation model","volume":"461","author":"Ding","year":"2021","journal-title":"Neurocomputing"},{"issue":"9","key":"2024041601501452900_ref50","doi-asserted-by":"crossref","first-page":"2042","DOI":"10.1021\/acs.jcim.5b00320","article-title":"Identification of protein-protein interactions by detecting correlated mutation at the interface","volume":"55","author":"Guo","year":"2015","journal-title":"J Chem Inf Model"},{"key":"2024041601501452900_ref51","article-title":"Laplacian regularized sparse representation based classifier for identifying DNA N4-methylcytosine sites via L2, 1\/2-matrix norm","volume":"20","author":"Ding","journal-title":"IEEE\/ACM Trans Comput Biol Bioinform"},{"issue":"1","key":"2024041601501452900_ref52","doi-asserted-by":"crossref","first-page":"btac715","DOI":"10.1093\/bioinformatics\/btac715","article-title":"sAMPpred-GAT: prediction of antimicrobial peptide by graph attention network and predicted peptide structure","volume":"39","author":"Yan","year":"2023","journal-title":"Bioinformatics"},{"issue":"21","key":"2024041601501452900_ref53","doi-asserted-by":"crossref","first-page":"5177","DOI":"10.1093\/bioinformatics\/btaa667","article-title":"IDP-Seq2Seq: identification of intrinsically disordered regions based on sequence to sequence learning","volume":"36","author":"Tang","year":"2021","journal-title":"Bioinformatics"},{"key":"2024041601501452900_ref54","doi-asserted-by":"crossref","first-page":"1281880","DOI":"10.3389\/fmed.2023.1281880","article-title":"Accurately identifying hemagglutinin using sequence information and machine learning methods","volume":"10","author":"Zou","year":"2023","journal-title":"Front Med (Lausanne)"},{"issue":"14","key":"2024041601501452900_ref55","article-title":"A first computational frame for recognizing heparin-binding","volume":"13","author":"Zhu","year":"2023","journal-title":"Protein Diagn (Basel)"},{"key":"2024041601501452900_ref56","doi-asserted-by":"crossref","first-page":"170","DOI":"10.1016\/j.neunet.2022.09.026","article-title":"Shared subspace-based radial basis function neural network for identifying ncRNAs subcellular localization","volume":"156","author":"Ding","year":"2022","journal-title":"Neural Netw"},{"issue":"20","key":"2024041601501452900_ref57","doi-asserted-by":"crossref","first-page":"e127","DOI":"10.1093\/nar\/gkz740","article-title":"BioSeq-Analysis2.0: an updated platform for analyzing DNA, RNA and protein sequences at sequence level and residue level based on machine learning approaches","volume":"47","author":"Liu","year":"2019","journal-title":"Nucleic Acids Res"},{"key":"2024041601501452900_ref58","doi-asserted-by":"crossref","first-page":"101911","DOI":"10.1016\/j.inffus.2023.101911","article-title":"Multi-correntropy fusion based fuzzy system for predicting DNA N4-methylcytosine sites","volume":"100","author":"Ding","year":"2023","journal-title":"Inf Fusion"},{"issue":"1","key":"2024041601501452900_ref59","first-page":"1","article-title":"Identify RNA-associated subcellular localizations based on multi-label learning using Chou\u2019s 5-steps rule","volume":"22","author":"Wang","year":"2021","journal-title":"BMC Genomics"},{"issue":"11","key":"2024041601501452900_ref60","doi-asserted-by":"crossref","first-page":"4754","DOI":"10.1109\/TFUZZ.2022.3159103","article-title":"C-loss based higher order fuzzy inference systems for identifying DNA N4-methylcytosine sites","volume":"30","author":"Ding","year":"2022","journal-title":"IEEE Trans Fuzzy Syst"}],"container-title":["Briefings in Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/25\/3\/bbae169\/57235620\/bbae169.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/25\/3\/bbae169\/57235620\/bbae169.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,4,16]],"date-time":"2024-04-16T01:50:56Z","timestamp":1713232256000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bib\/article\/doi\/10.1093\/bib\/bbae169\/7645840"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,3,27]]},"references-count":60,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2024,3,27]]}},"URL":"https:\/\/doi.org\/10.1093\/bib\/bbae169","relation":{},"ISSN":["1467-5463","1477-4054"],"issn-type":[{"value":"1467-5463","type":"print"},{"value":"1477-4054","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2024,5]]},"published":{"date-parts":[[2024,3,27]]},"article-number":"bbae169"}}