{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,17]],"date-time":"2026-07-17T02:22:40Z","timestamp":1784254960074,"version":"3.55.0"},"reference-count":59,"publisher":"Oxford University Press (OUP)","issue":"3","license":[{"start":{"date-parts":[[2023,4,29]],"date-time":"2023-04-29T00:00:00Z","timestamp":1682726400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/pages\/standard-publication-reuse-rights"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62002234"],"award-info":[{"award-number":["62002234"]}],"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":["62131004"],"award-info":[{"award-number":["62131004"]}],"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":["81971015"],"award-info":[{"award-number":["81971015"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100021171","name":"Guangdong Basic and Applied Basic Research Foundation","doi-asserted-by":"publisher","award":["2019A1515111180"],"award-info":[{"award-number":["2019A1515111180"]}],"id":[{"id":"10.13039\/501100021171","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Natural Science Foundation of SZU","award":["827-000393"],"award-info":[{"award-number":["827-000393"]}]},{"name":"Special Projects of the Central Government in Guidance of Local Science and Technology Development","award":["2022JH6\/100100025"],"award-info":[{"award-number":["2022JH6\/100100025"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,5,19]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Single-cell RNA sequencing (scRNA-seq) technology attracts extensive attention in the biomedical field. It can be used to measure gene expression and analyze the transcriptome at the single-cell level, enabling the identification of cell types based on unsupervised clustering. Data imputation and dimension reduction are conducted before clustering because scRNA-seq has a high \u2018dropout\u2019 rate, noise and linear inseparability. However, independence of dimension reduction, imputation and clustering cannot fully characterize the pattern of the scRNA-seq data, resulting in poor clustering performance. Herein, we propose a novel and accurate algorithm, SSNMDI, that utilizes a joint learning approach to simultaneously perform imputation, dimensionality reduction and cell clustering in a non-negative matrix factorization (NMF) framework. In addition, we integrate the cell annotation as prior information, then transform the joint learning into a semi-supervised NMF model. Through experiments on 14 datasets, we demonstrate that SSNMDI has a faster convergence speed, better dimensionality reduction performance and a more accurate cell clustering performance than previous methods, providing an accurate and robust strategy for analyzing scRNA-seq data. Biological analysis are also conducted to validate the biological significance of our method, including pseudotime analysis, gene ontology and survival analysis. We believe that we are among the first to introduce imputation, partial label information, dimension reduction and clustering to the single-cell field.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>The source code for SSNMDI is available at https:\/\/github.com\/yushanqiu\/SSNMDI.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bib\/bbad149","type":"journal-article","created":{"date-parts":[[2023,5,1]],"date-time":"2023-05-01T03:29:45Z","timestamp":1682911785000},"source":"Crossref","is-referenced-by-count":21,"title":["SSNMDI: a novel joint learning model of semi-supervised non-negative matrix factorization and data imputation for clustering of single-cell RNA-seq data"],"prefix":"10.1093","volume":"24","author":[{"given":"Yushan","family":"Qiu","sequence":"first","affiliation":[{"name":"College of Mathematics and Statistics , Shenzhen University, 518000, Guangdong , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chang","family":"Yan","sequence":"additional","affiliation":[{"name":"College of Mathematics and Statistics , Shenzhen University, 518000, Guangdong , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pu","family":"Zhao","sequence":"additional","affiliation":[{"name":"College of Life and Health Sciences , Northeastern University, Shenyang, 110169 , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Quan","family":"Zou","sequence":"additional","affiliation":[{"name":"Institute of Fundamental and Frontier Sciences , University of Electronic Science and Technology of China, Chengdu, 610056 , China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2023,4,29]]},"reference":[{"issue":"5","key":"2023052022215691100_ref1","doi-asserted-by":"crossref","first-page":"377","DOI":"10.1038\/nmeth.1315","article-title":"Mrna-seq whole-transcriptome analysis of a single cell","volume":"6","author":"Tang","year":"2009","journal-title":"Nat Methods"},{"issue":"6392","key":"2023052022215691100_ref2","doi-asserted-by":"crossref","first-page":"981","DOI":"10.1126\/science.aar4362","article-title":"Single-cell mapping of gene expression landscapes and lineage in the zebrafish embryo","volume":"360","author":"Wagner","year":"2018","journal-title":"Science"},{"issue":"7","key":"2023052022215691100_ref3","doi-asserted-by":"crossref","first-page":"847","DOI":"10.1038\/s41556-018-0123-2","article-title":"Single-cell multi-omics sequencing of human early embryos","volume":"20","author":"Li","year":"2018","journal-title":"Nat Cell Biol"},{"issue":"1","key":"2023052022215691100_ref4","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1038\/nbt.4038","article-title":"Integrative single-cell analysis of transcriptional and epigenetic states in the human adult brain","volume":"36","author":"Lake","year":"2018","journal-title":"Nat Biotechnol"},{"issue":"7","key":"2023052022215691100_ref5","doi-asserted-by":"crossref","first-page":"1626","DOI":"10.1016\/j.cell.2020.04.055","article-title":"Single-cell mapping of human brain cancer reveals tumor-specific instruction of tissue-invading leukocytes","volume":"181","author":"Friebel","year":"2020","journal-title":"Cell"},{"issue":"6282","key":"2023052022215691100_ref6","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1126\/science.aad0501","article-title":"Dissecting the multicellular ecosystem of metastatic melanoma by single-cell rna-seq","volume":"352","author":"Tirosh","year":"2016","journal-title":"Science"},{"issue":"7697","key":"2023052022215691100_ref7","doi-asserted-by":"crossref","first-page":"538","DOI":"10.1038\/nature25981","article-title":"The cis-regulatory dynamics of embryonic development at single-cell resolution","volume":"555","author":"Cusanovich","year":"2018","journal-title":"Nature"},{"issue":"5","key":"2023052022215691100_ref8","doi-asserted-by":"crossref","first-page":"1091","DOI":"10.1016\/j.cell.2018.02.001","article-title":"Mapping the mouse cell atlas by microwell-seq","volume":"172","author":"Han","year":"2018","journal-title":"Cell"},{"issue":"5","key":"2023052022215691100_ref9","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1038\/s41576-018-0088-9","article-title":"Challenges in unsupervised clustering of single-cell rna-seq data","volume":"20","author":"Kiselev","year":"2019","journal-title":"Nat Rev Genet"},{"issue":"11","key":"2023052022215691100_ref10","doi-asserted-by":"crossref","first-page":"1093","DOI":"10.1038\/nmeth.2645","article-title":"Accounting for technical noise in single-cell rna-seq experiments","volume":"10","author":"Brennecke","year":"2013","journal-title":"Nat Methods"},{"issue":"6","key":"2023052022215691100_ref11","doi-asserted-by":"crossref","first-page":"2714","DOI":"10.1109\/TCBB.2020.2992605","article-title":"Unsupervised learning framework with multidimensional scaling in predicting epithelial-mesenchymal transitions","volume":"18","author":"Qiu","year":"2020","journal-title":"IEEE\/ACM Trans Comput Biol Bioinform"},{"issue":"5","key":"2023052022215691100_ref12","doi-asserted-by":"crossref","first-page":"483","DOI":"10.1038\/nmeth.4236","article-title":"Sc3: consensus clustering of single-cell rna-seq data","volume":"14","author":"Kiselev","year":"2017","journal-title":"Nat Methods"},{"issue":"1","key":"2023052022215691100_ref13","first-page":"1","article-title":"Pcareduce: hierarchical clustering of single cell transcriptional profiles","volume":"17","author":"Yau","year":"2016","journal-title":"BMC Bioinform"},{"issue":"3","key":"2023052022215691100_ref14","doi-asserted-by":"crossref","first-page":"100882","DOI":"10.1016\/j.isci.2020.100882","article-title":"Sccatch: automatic annotation on cell types of clusters from single-cell rna sequencing data","volume":"23","author":"Shao","year":"2020","journal-title":"Iscience"},{"key":"2023052022215691100_ref15","first-page":"111591","article-title":"Magic: a diffusion-based imputation method reveals gene-gene interactions in single-cell rna-sequencing data","author":"van Dijk","year":"2017","journal-title":"BioRxiv"},{"issue":"1","key":"2023052022215691100_ref16","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12859-018-2226-y","article-title":"Drimpute: imputing dropout events in single cell rna sequencing data","volume":"19","author":"Gong","year":"2018","journal-title":"BMC Bioinform"},{"issue":"2","key":"2023052022215691100_ref17","doi-asserted-by":"crossref","first-page":"1700232","DOI":"10.1002\/pmic.201700232","article-title":"Simlr: a tool for large-scale genomic analyses by multi-kernel learning","volume":"18","author":"Wang","year":"2018","journal-title":"Proteomics"},{"issue":"6755","key":"2023052022215691100_ref18","doi-asserted-by":"crossref","first-page":"788","DOI":"10.1038\/44565","article-title":"Learning the parts of objects by non-negative matrix factorization","volume":"401","author":"Lee","year":"1999","journal-title":"Nature"},{"issue":"30","key":"2023052022215691100_ref19","doi-asserted-by":"crossref","first-page":"7723","DOI":"10.1073\/pnas.1805681115","article-title":"Integrative analysis of single-cell genomics data by coupled nonnegative matrix factorizations","volume":"115","author":"Duren","year":"2018","journal-title":"Proc Natl Acad Sci"},{"issue":"16","key":"2023052022215691100_ref20","doi-asserted-by":"crossref","first-page":"2809","DOI":"10.1093\/bioinformatics\/bty1056","article-title":"Single-cell rna-seq interpretations using evolutionary multiobjective ensemble pruning","volume":"35","author":"Li","year":"2019","journal-title":"Bioinformatics"},{"key":"2023052022215691100_ref21","doi-asserted-by":"crossref","first-page":"2007","DOI":"10.1109\/ICIP.2004.1421476","article-title":"Color channel encoding with nmf for face recognition","volume-title":"2004 International Conference on Image Processing, 2004. ICIP\u201904","author":"Rajapakse","year":"2004"},{"key":"2023052022215691100_ref22","doi-asserted-by":"crossref","first-page":"1675","DOI":"10.1145\/2939672.2939874","article-title":"Interpretable decision sets: a joint framework for description and prediction","volume-title":"Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining","author":"Lakkaraju","year":"2016"},{"issue":"12","key":"2023052022215691100_ref23","doi-asserted-by":"crossref","first-page":"3825","DOI":"10.1093\/bioinformatics\/btaa231","article-title":"Joint learning dimension reduction and clustering of single-cell rna-sequencing data","volume":"36","author":"Wenming","year":"2020","journal-title":"Bioinformatics"},{"issue":"7","key":"2023052022215691100_ref24","doi-asserted-by":"crossref","first-page":"1299","DOI":"10.1109\/TPAMI.2011.217","article-title":"Constrained nonnegative matrix factorization for image representation","volume":"34","author":"Liu","year":"2011","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"3","key":"2023052022215691100_ref25","doi-asserted-by":"crossref","first-page":"lqaa064","DOI":"10.1093\/nargab\/lqaa064","article-title":"Dimensionality reduction for single cell rna sequencing data using constrained robust non-negative matrix factorization","volume":"2","author":"Zhang","year":"2020","journal-title":"NAR Genomics Bioinform"},{"issue":"1","key":"2023052022215691100_ref26","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1109\/TPAMI.2008.277","article-title":"Convex and semi-nonnegative matrix factorizations","volume":"32","author":"Ding","year":"2008","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"8","key":"2023052022215691100_ref27","first-page":"1548","article-title":"Graph regularized nonnegative matrix factorization for data representation","volume":"33","author":"Cai","year":"2010","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"1","key":"2023052022215691100_ref28","doi-asserted-by":"crossref","first-page":"174","DOI":"10.1186\/s13059-017-1305-0","article-title":"Splatter: simulation of single-cell rna sequencing data","volume":"18","author":"Zappia","year":"2017","journal-title":"Genome Biol"},{"issue":"1","key":"2023052022215691100_ref29","first-page":"1","article-title":"An accurate and robust imputation method scimpute for single-cell rna-seq data","volume":"9","author":"Wei Vivian Li and Jingyi Jessica Li","year":"2018","journal-title":"Nat Commun"},{"issue":"9","key":"2023052022215691100_ref30","doi-asserted-by":"crossref","first-page":"1131","DOI":"10.1038\/nsmb.2660","article-title":"Single-cell rna-seq profiling of human preimplantation embryos and embryonic stem cells","volume":"20","author":"Yan","year":"2013","journal-title":"Nat Struct Mol Biol"},{"issue":"6167","key":"2023052022215691100_ref31","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1126\/science.1245316","article-title":"Single-cell rna-seq reveals dynamic, random monoallelic gene expression in mammalian cells","volume":"343","author":"Deng","year":"2014","journal-title":"Science"},{"issue":"1","key":"2023052022215691100_ref32","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/ncomms14049","article-title":"Massively parallel digital transcriptional profiling of single cells","volume":"8","author":"Zheng","year":"2017","journal-title":"Nat Commun"},{"issue":"1","key":"2023052022215691100_ref33","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1038\/nn.3881","article-title":"Unbiased classification of sensory neuron types by large-scale single-cell rna sequencing","volume":"18","author":"Usoskin","year":"2015","journal-title":"Nat Neurosci"},{"issue":"4","key":"2023052022215691100_ref34","doi-asserted-by":"crossref","first-page":"471","DOI":"10.1016\/j.stem.2015.09.011","article-title":"Single cell rna-sequencing of pluripotent states unlocks modular transcriptional variation","volume":"17","author":"Kolodziejczyk","year":"2015","journal-title":"Cell Stem Cell"},{"issue":"4","key":"2023052022215691100_ref35","doi-asserted-by":"crossref","first-page":"346","DOI":"10.1016\/j.cels.2016.08.011","article-title":"A single-cell transcriptomic map of the human and mouse pancreas reveals inter-and intra-cell population structure","volume":"3","author":"Baron","year":"2016","journal-title":"Cell Syst"},{"issue":"7568","key":"2023052022215691100_ref36","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1038\/nature14966","article-title":"Single-cell messenger rna sequencing reveals rare intestinal cell types","volume":"525","author":"Gr\u00fcn","year":"2015","journal-title":"Nature"},{"key":"2023052022215691100_ref37","doi-asserted-by":"crossref","DOI":"10.1101\/632216","article-title":"Systematic comparative analysis of single cell rna-sequencing methods","author":"Ding","year":"2019"},{"issue":"7680","key":"2023052022215691100_ref38","doi-asserted-by":"crossref","first-page":"333","DOI":"10.1038\/nature24489","article-title":"A single-cell survey of the small intestinal epithelium","volume":"551","author":"Haber","year":"2017","journal-title":"Nature"},{"issue":"13","key":"2023052022215691100_ref39","doi-asserted-by":"crossref","first-page":"3227","DOI":"10.1016\/j.celrep.2017.03.004","article-title":"Single-cell rna-seq reveals hypothalamic cell diversity","volume":"18","author":"Chen","year":"2017","journal-title":"Cell Rep"},{"issue":"1","key":"2023052022215691100_ref40","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1038\/nbt.4314","article-title":"Dimensionality reduction for visualizing single-cell data using umap","volume":"37","author":"Becht","year":"2019","journal-title":"Nat Biotechnol"},{"issue":"5","key":"2023052022215691100_ref41","doi-asserted-by":"crossref","first-page":"411","DOI":"10.1038\/nbt.4096","article-title":"Integrating single-cell transcriptomic data across different conditions, technologies, and species","volume":"36","author":"Butler","year":"2018","journal-title":"Nat Biotechnol"},{"issue":"Supplement_1","key":"2023052022215691100_ref42","doi-asserted-by":"crossref","first-page":"i51","DOI":"10.1093\/bioinformatics\/btab286","article-title":"Callr: a semi-supervised cell-type annotation method for single-cell rna sequencing data","volume":"37","author":"Wei","year":"2021","journal-title":"Bioinformatics"},{"key":"2023052022215691100_ref43","doi-asserted-by":"crossref","first-page":"e10091","DOI":"10.7717\/peerj.10091","article-title":"A robust semi-supervised nmf model for single cell rna-seq data","volume":"8","author":"Peng","year":"2020","journal-title":"PeerJ"},{"issue":"1","key":"2023052022215691100_ref44","doi-asserted-by":"crossref","first-page":"5853","DOI":"10.1038\/s41467-020-19465-7","article-title":"Ensemble dimensionality reduction and feature gene extraction for single-cell rna-seq data","volume":"11","author":"Sun","year":"2020","journal-title":"Nat Commun"},{"issue":"1","key":"2023052022215691100_ref45","first-page":"100382","article-title":"Graph embedding and gaussian mixture variational autoencoder network for end-to-end analysis of single-cell rna sequencing data","volume":"3","author":"Xu","year":"2023","journal-title":"Methods"},{"key":"2023052022215691100_ref46","doi-asserted-by":"crossref","first-page":"1141","DOI":"10.12688\/f1000research.15666.3","article-title":"A systematic performance evaluation of clustering methods for single-cell rna-seq data","volume":"7","author":"Du\u00f2","year":"2020","journal-title":"F1000Research"},{"key":"2023052022215691100_ref47","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1016\/0377-0427(87)90125-7","article-title":"Silhouettes: a graphical aid to the interpretation and validation of cluster analysis","volume":"20","author":"Rousseeuw","year":"1987","journal-title":"J Comput Appl Math"},{"issue":"4","key":"2023052022215691100_ref48","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1038\/nbt.2859","article-title":"The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells","volume":"32","author":"Trapnell","year":"2014","journal-title":"Nat Biotechnol"},{"issue":"1","key":"2023052022215691100_ref49","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13059-016-1033-x","article-title":"Single-cell rna-seq reveals novel regulators of human embryonic stem cell differentiation to definitive endoderm","volume":"17","author":"Chu","year":"2016","journal-title":"Genome Biol"},{"issue":"51","key":"2023052022215691100_ref50","doi-asserted-by":"crossref","first-page":"15672","DOI":"10.1073\/pnas.1520760112","article-title":"Human cerebral organoids recapitulate gene expression programs of fetal neocortex development","volume":"112","author":"","year":"2015","journal-title":"Proc Natl Acad Sci"},{"issue":"12","key":"2023052022215691100_ref51","doi-asserted-by":"crossref","first-page":"738","DOI":"10.1038\/s41580-019-0172-9","article-title":"Mechanisms of 3d cell migration","volume":"20","author":"Yamada","year":"2019","journal-title":"Nat Rev Mol Cell Biol"},{"issue":"3","key":"2023052022215691100_ref52","first-page":"167","article-title":"Cell migration: implications for repair and regeneration in joint disease. Nature reviews","volume":"15","author":"Feini","year":"2019","journal-title":"Rheumatology"},{"issue":"1","key":"2023052022215691100_ref53","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1038\/s41556-017-0012-0","article-title":"Mechanoreciprocity in cell migration","volume":"20","author":"Van Helvert","year":"2018","journal-title":"Nat Cell Biol"},{"key":"2023052022215691100_ref54","doi-asserted-by":"crossref","DOI":"10.1016\/j.tcb.2021.10.004","article-title":"The amoeboid state as part of the epithelial-to-mesenchymal transition programme","volume":"32","author":"Graziani","year":"2022","journal-title":"Trends Cell Biol"},{"issue":"9","key":"2023052022215691100_ref55","doi-asserted-by":"crossref","first-page":"775","DOI":"10.1016\/j.trecan.2020.03.011","article-title":"Emerging mechanisms by which emt programs control stemness","volume":"6","author":"Wilson","year":"2020","journal-title":"Trends cancer"},{"issue":"6514","key":"2023052022215691100_ref56","doi-asserted-by":"crossref","first-page":"eaba2894","DOI":"10.1126\/science.aba2894","article-title":"The nucleus acts as a ruler tailoring cell responses to spatial constraints","volume":"370","author":"Lomakin","year":"2020","journal-title":"Science"},{"issue":"3","key":"2023052022215691100_ref57","doi-asserted-by":"crossref","first-page":"392","DOI":"10.1016\/j.cub.2016.11.057","article-title":"Hypoxia induces a hif-1-dependent transition from collective-to-amoeboid dissemination in epithelial cancer cells","volume":"27","author":"Lehmann","year":"2017","journal-title":"Curr Biol"},{"issue":"1","key":"2023052022215691100_ref58","first-page":"1","article-title":"Inflammation and tumor progression: Signaling pathways and targeted intervention","volume":"6","author":"Zhao","year":"2021","journal-title":"Signal Transduct Target Ther"},{"issue":"7775","key":"2023052022215691100_ref59","doi-asserted-by":"crossref","first-page":"526","DOI":"10.1038\/s41586-019-1576-6","article-title":"Synaptic proximity enables nmdar signalling to promote brain metastasis","volume":"573","author":"Zeng","year":"2019","journal-title":"Nature"}],"container-title":["Briefings in Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/24\/3\/bbad149\/50410240\/bbad149.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/24\/3\/bbad149\/50410240\/bbad149.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,5,20]],"date-time":"2023-05-20T22:23:15Z","timestamp":1684621395000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bib\/article\/doi\/10.1093\/bib\/bbad149\/7147025"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,4,29]]},"references-count":59,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2023,5,19]]}},"URL":"https:\/\/doi.org\/10.1093\/bib\/bbad149","relation":{},"ISSN":["1467-5463","1477-4054"],"issn-type":[{"value":"1467-5463","type":"print"},{"value":"1477-4054","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2023,5]]},"published":{"date-parts":[[2023,4,29]]},"article-number":"bbad149"}}