{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,18]],"date-time":"2026-04-18T15:28:35Z","timestamp":1776526115777,"version":"3.51.2"},"reference-count":51,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,12,31]],"date-time":"2025-12-31T00:00:00Z","timestamp":1767139200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,2,2]],"date-time":"2026-02-02T00:00:00Z","timestamp":1769990400000},"content-version":"vor","delay-in-days":33,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"the Henan Province Science Foundation of Excellent Young Scholars","award":["242300421171"],"award-info":[{"award-number":["242300421171"]}]},{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62106066"],"award-info":[{"award-number":["62106066"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62476229"],"award-info":[{"award-number":["62476229"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"the Henan Province Science Foundation of Young Scholars","award":["242300420678"],"award-info":[{"award-number":["242300420678"]}]},{"name":"the Science and Technology Research Program of Chongqing Municipal Education Commission","award":["KJQN202200207"],"award-info":[{"award-number":["KJQN202200207"]}]},{"name":"the Open Fund of Key Laboratory of Cyber-Physical Fusion Intelligent Computing (South-Central Minzu University), State Ethnic Affairs Commission","award":["CPFIC202303"],"award-info":[{"award-number":["CPFIC202303"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Big Data"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Feature selection in high-dimensional data is an important part of the data mining process and is widely used in bioinformatics, statistics and image processing fields. Successfully selecting informative features can significantly improve learning accuracy and improve result comprehensibility. However, it is a challenging problem to select features accurately and efficiently from high-dimensional data. In this paper, we propose a Weighted Sparse Regression with Mutual Information (WSRMI) for selecting structural features. Differing from traditional sparse feature selection models that focus solely on either feature correlations or feature importance, the proposed model integrates both aspects through a mutual-information-based weighting mechanism. The proposed model can be effectively applied to regression and binary classification tasks, making it more general and practical for real-world applications. The proposed model is statistically compared with several existing classical models over randomly generated classification and benchmark datasets. Experimental results show that the proposed model is more effective at selecting the informative features with a superior prediction performance than the comparative ones.<\/jats:p>","DOI":"10.1186\/s40537-025-01344-x","type":"journal-article","created":{"date-parts":[[2025,12,30]],"date-time":"2025-12-30T23:45:52Z","timestamp":1767138352000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Structural feature selection via weighted sparse regression with mutual information"],"prefix":"10.1186","volume":"13","author":[{"given":"Yadi","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yulin","family":"Xie","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sufang","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bingbing","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hangjun","family":"Che","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,12,31]]},"reference":[{"issue":"23","key":"1344_CR1","doi-asserted-by":"publisher","first-page":"9326","DOI":"10.1016\/j.eswa.2015.08.016","volume":"42","author":"ZY Algamal","year":"2015","unstructured":"Algamal ZY, Lee MH. Penalized logistic regression with the adaptive lasso for gene selection in high-dimensional cancer classification. Expert Syst Appl. 2015;42(23):9326\u201332.","journal-title":"Expert Syst Appl"},{"issue":"27","key":"1344_CR2","doi-asserted-by":"publisher","first-page":"42617","DOI":"10.1007\/s11042-023-15143-0","volume":"82","author":"S Asghari","year":"2023","unstructured":"Asghari S, Nematzadeh H, Akbari E, et al. Mutual information-based filter hybrid feature selection method for medical datasets using feature clustering. Multimedia Tools Appl. 2023;82(27):42617\u201339.","journal-title":"Multimedia Tools Appl"},{"key":"1344_CR3","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2023.106080","volume":"122","author":"J Ba","year":"2023","unstructured":"Ba J, Wang P, Yang X, et al. Glee: a granularity filter for feature selection. Eng Appl Artif Intell. 2023;122:106080.","journal-title":"Eng Appl Artif Intell"},{"issue":"3","key":"1344_CR4","doi-asserted-by":"publisher","first-page":"1070","DOI":"10.1016\/j.ejor.2024.03.035","volume":"316","author":"M Barbato","year":"2024","unstructured":"Barbato M, Ceselli A. Mathematical programming for simultaneous feature selection and outlier detection under l1 norm. Eur J Oper Res. 2024;316(3):1070\u201384.","journal-title":"Eur J Oper Res"},{"issue":"2","key":"1344_CR5","doi-asserted-by":"publisher","first-page":"782","DOI":"10.1016\/j.ejor.2021.05.049","volume":"297","author":"L Bottmer","year":"2022","unstructured":"Bottmer L, Croux C, Wilms I. Sparse regression for large data sets with outliers. Eur J Oper Res. 2022;297(2):782\u201394.","journal-title":"Eur J Oper Res"},{"key":"1344_CR6","unstructured":"Cover TM, Thomas JA. Elements of information theory; 2003."},{"key":"1344_CR7","doi-asserted-by":"crossref","unstructured":"Dash M, Choi K, Scheuermann P, et\u00a0al. Feature selection for clustering-a filter solution. In: 2002 IEEE international conference on data mining, 2002. Proceedings, IEEE; 2002:115\u201322.","DOI":"10.1109\/ICDM.2002.1183893"},{"issue":"4","key":"1344_CR8","doi-asserted-by":"publisher","first-page":"1284","DOI":"10.1016\/j.csda.2008.11.007","volume":"53","author":"ZJ Daye","year":"2009","unstructured":"Daye ZJ, Jeng XJ. Shrinkage and model selection with correlated variables via weighted fusion. Comput Stat Data Anal. 2009;53(4):1284\u201398.","journal-title":"Comput Stat Data Anal"},{"key":"1344_CR9","unstructured":"Duda R, Hart P, Stork D, et\u00a0al. Pattern classification, chapter nonparametric techniques; 2000."},{"issue":"6","key":"1344_CR10","doi-asserted-by":"publisher","first-page":"1990","DOI":"10.1109\/TSMCB.2012.2237394","volume":"43","author":"C Freeman","year":"2013","unstructured":"Freeman C, Kuli\u0107 D, Basir O. Feature-selected tree-based classification. IEEE Trans Cybern. 2013;43(6):1990\u20132004.","journal-title":"IEEE Trans Cybern"},{"issue":"2","key":"1344_CR11","doi-asserted-by":"publisher","first-page":"302","DOI":"10.1214\/07-AOAS131","volume":"1","author":"J Friedman","year":"2007","unstructured":"Friedman J, Hastie T, Hofling H, et al. Pathwise coordinate optimization. Ann Appl Stat. 2007;1(2):302\u201332.","journal-title":"Ann Appl Stat"},{"key":"1344_CR12","doi-asserted-by":"crossref","unstructured":"Goutte C, Gaussier E. A probabilistic interpretation of precision, recall and f-score, with implication for evaluation. In: European conference on information retrieval. Springer; 2005. p. 345\u201359.","DOI":"10.1007\/978-3-540-31865-1_25"},{"issue":"6","key":"1344_CR13","doi-asserted-by":"publisher","first-page":"4519","DOI":"10.1007\/s10462-019-09800-w","volume":"53","author":"E Hancer","year":"2020","unstructured":"Hancer E, Xue B, Zhang M. A survey on feature selection approaches for clustering. Artif Intell Rev. 2020;53(6):4519\u201345.","journal-title":"Artif Intell Rev"},{"key":"1344_CR14","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1016\/j.apm.2021.04.018","volume":"98","author":"RA Ibrahim","year":"2021","unstructured":"Ibrahim RA, Abd Elaziz M, Ewees AA, et al. New feature selection paradigm based on hyper-heuristic technique. Appl Math Model. 2021;98:14\u201337.","journal-title":"Appl Math Model"},{"issue":"2","key":"1344_CR15","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3309541","volume":"13","author":"B Jiang","year":"2019","unstructured":"Jiang B, Li C, Rijke MD, et al. Probabilistic feature selection and classification vector machine. ACM Trans Knowl Discov Data. 2019;13(2):1\u201327.","journal-title":"ACM Trans Knowl Discov Data"},{"key":"1344_CR16","doi-asserted-by":"publisher","first-page":"71180","DOI":"10.1109\/ACCESS.2023.3293649","volume":"11","author":"LR Kalabarige","year":"2023","unstructured":"Kalabarige LR, Rao RS, Pais AR, et al. A boosting-based hybrid feature selection and multi-layer stacked ensemble learning model to detect phishing websites. IEEE Access. 2023;11:71180\u201393.","journal-title":"IEEE Access"},{"issue":"6","key":"1344_CR17","doi-asserted-by":"publisher","first-page":"1193","DOI":"10.1007\/s00521-012-0885-6","volume":"22","author":"J Li","year":"2013","unstructured":"Li J, Jia Y, Zhao Z. Partly adaptive elastic net and its application to microarray classification. Neural Comput Appl. 2013;22(6):1193\u2013200.","journal-title":"Neural Comput Appl"},{"key":"1344_CR18","doi-asserted-by":"publisher","first-page":"364","DOI":"10.1016\/j.compbiolchem.2019.04.010","volume":"80","author":"J Li","year":"2019","unstructured":"Li J, Wang Y, Xiao H, et al. Gene selection of rat hepatocyte proliferation using adaptive sparse group lasso with weighted gene co-expression network analysis. Comput Biol Chem. 2019;80:364\u201373.","journal-title":"Comput Biol Chem"},{"key":"1344_CR19","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2020.2982445","author":"X Li","year":"2020","unstructured":"Li X, Wang Y, Ruiz R. A survey on sparse learning models for feature selection. IEEE Trans Cybern. 2020. https:\/\/doi.org\/10.1109\/TCYB.2020.2982445.","journal-title":"IEEE Trans Cybern"},{"issue":"9","key":"1344_CR20","doi-asserted-by":"publisher","DOI":"10.1007\/s11432-020-3063-0","volume":"64","author":"Z Li","year":"2021","unstructured":"Li Z, Tang J. Semi-supervised local feature selection for data classification. Sci China Inf Sci. 2021;64(9):192108.","journal-title":"Sci China Inf Sci"},{"key":"1344_CR21","doi-asserted-by":"crossref","unstructured":"Liao C, Li S, Luo Z. Gene selection using Wilcoxon rank sum test and support vector machine for cancer classification. In: International conference on computational and information science. Springer; 2006. p. 57\u201366.","DOI":"10.1007\/978-3-540-74377-4_7"},{"issue":"1","key":"1344_CR22","doi-asserted-by":"publisher","first-page":"312","DOI":"10.1109\/TCBB.2017.2767589","volume":"16","author":"C Liu","year":"2017","unstructured":"Liu C, San WH. Structured penalized logistic regression for gene selection in gene expression data analysis. IEEE ACM Trans Comput Biol Bioinform. 2017;16(1):312\u201321.","journal-title":"IEEE ACM Trans Comput Biol Bioinform"},{"issue":"1","key":"1344_CR23","doi-asserted-by":"publisher","first-page":"312","DOI":"10.1109\/TCBB.2017.2767589","volume":"6","author":"C Liu","year":"2019","unstructured":"Liu C, San WH. Structured penalized logistic regression for gene selection in gene expression data analysis. IEEE ACM Trans Comput Biol Bioinform. 2019;6(1):312\u201321.","journal-title":"IEEE ACM Trans Comput Biol Bioinform"},{"issue":"1","key":"1344_CR24","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1186\/s40537-024-00887-9","volume":"11","author":"EM Maseno","year":"2024","unstructured":"Maseno EM, Wang Z. Hybrid wrapper feature selection method based on genetic algorithm and extreme learning machine for intrusion detection. J Big Data. 2024;11(1):24.","journal-title":"J Big Data"},{"key":"1344_CR25","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2023.107136","volume":"126","author":"A Moslemi","year":"2023","unstructured":"Moslemi A. A tutorial-based survey on feature selection: recent advancements on feature selection. Eng Appl Artif Intell. 2023;126:107136.","journal-title":"Eng Appl Artif Intell"},{"key":"1344_CR26","unstructured":"Nie F, Huang H, Cai X, et\u00a0al. Efficient and robust feature selection via joint $$\\ell$$ 2, 1-norms minimization. Adv Neural Inf Process Syst. 2010;23."},{"issue":"1","key":"1344_CR27","doi-asserted-by":"publisher","first-page":"153","DOI":"10.1007\/s44196-023-00319-1","volume":"16","author":"RE Nogales","year":"2023","unstructured":"Nogales RE, Benalc\u00e1zar ME. Analysis and evaluation of feature selection and feature extraction methods. Int J Comput Intell Syst. 2023;16(1):153.","journal-title":"Int J Comput Intell Syst"},{"key":"1344_CR28","unstructured":"Press W, Teukolsky S, Vetterling W, et\u00a0al. Numerical recipes in C; 1996."},{"key":"1344_CR29","doi-asserted-by":"publisher","DOI":"10.3389\/fbinf.2022.927312","volume":"2","author":"N Pudjihartono","year":"2022","unstructured":"Pudjihartono N, Fadason T, Kempa-Liehr AW, et al. A review of feature selection methods for machine learning-based disease risk prediction. Front Bioinform. 2022;2:927312.","journal-title":"Front Bioinform"},{"issue":"5","key":"1344_CR30","doi-asserted-by":"publisher","first-page":"3473","DOI":"10.1007\/s10462-020-09928-0","volume":"54","author":"P Ray","year":"2021","unstructured":"Ray P, Reddy SS, Banerjee T. Various dimension reduction techniques for high dimensional data analysis: a review. Artif Intell Rev. 2021;54(5):3473\u2013515.","journal-title":"Artif Intell Rev"},{"issue":"3","key":"1344_CR31","doi-asserted-by":"publisher","first-page":"431","DOI":"10.1109\/TNN.2007.909535","volume":"19","author":"E Romero","year":"2008","unstructured":"Romero E, Sopena JM. Performing feature selection with multilayer perceptrons. IEEE Trans Neural Netw Learn Syst. 2008;19(3):431\u201341.","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"1344_CR32","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.122556","volume":"240","author":"M Samareh-Jahani","year":"2024","unstructured":"Samareh-Jahani M, Saberi-Movahed F, Eftekhari M, et al. Low-redundant unsupervised feature selection based on data structure learning and feature orthogonalization. Expert Syst Appl. 2024;240:122556.","journal-title":"Expert Syst Appl"},{"issue":"6","key":"1344_CR33","doi-asserted-by":"publisher","first-page":"961","DOI":"10.1089\/106652703322756177","volume":"10","author":"MR Segal","year":"2003","unstructured":"Segal MR, Dahlquist KD, Conklin BR. Regression approaches for microarray data analysis. J Comput Biol. 2003;10(6):961\u201380.","journal-title":"J Comput Biol"},{"key":"1344_CR34","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.122884","volume":"243","author":"Z Sun","year":"2024","unstructured":"Sun Z, Xie H, Liu J, et al. Multi-label feature selection via adaptive dual-graph optimization. Expert Syst Appl. 2024;243:122884.","journal-title":"Expert Syst Appl"},{"issue":"1","key":"1344_CR35","doi-asserted-by":"publisher","first-page":"267","DOI":"10.1111\/j.2517-6161.1996.tb02080.x","volume":"58","author":"R Tibshirani","year":"1996","unstructured":"Tibshirani R. Regression shrinkage and selection via the lasso. J R Stat Soc Ser B Stat Methodol. 1996;58(1):267\u201388.","journal-title":"J R Stat Soc Ser B Stat Methodol"},{"issue":"1","key":"1344_CR36","doi-asserted-by":"publisher","first-page":"267","DOI":"10.1111\/j.2517-6161.1996.tb02080.x","volume":"58","author":"R Tibshirani","year":"1996","unstructured":"Tibshirani R. Regression shrinkage and selection via the lasso. JR Stat Soc. 1996;58(1):267\u201388.","journal-title":"JR Stat Soc"},{"issue":"4","key":"1344_CR37","doi-asserted-by":"publisher","first-page":"828","DOI":"10.1109\/TKDE.2017.2650906","volume":"29","author":"J Wang","year":"2017","unstructured":"Wang J, Wei JM, Yang Z, et al. Feature selection by maximizing independent classification information. IEEE Trans Knowl Data Eng. 2017;29(4):828\u201341.","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"1344_CR38","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2025.110764","volume":"152","author":"M Wang","year":"2025","unstructured":"Wang M, Ge H, Wang X, et al. An evolutionary multitasking algorithm for multi-objective feature selection using dual-perspective reduction. Eng Appl Artif Intell. 2025;152:110764.","journal-title":"Eng Appl Artif Intell"},{"issue":"8","key":"1344_CR39","doi-asserted-by":"publisher","first-page":"2860","DOI":"10.1109\/TCYB.2018.2829811","volume":"49","author":"Y Wang","year":"2019","unstructured":"Wang Y, Li X, Ruiz R. Weighted general group lasso for gene selection in cancer classification. IEEE Trans Cybern. 2019;49(8):2860\u201373.","journal-title":"IEEE Trans Cybern"},{"key":"1344_CR40","doi-asserted-by":"publisher","first-page":"286","DOI":"10.1016\/j.apm.2019.01.044","volume":"71","author":"Y Wang","year":"2019","unstructured":"Wang Y, Yang XG, Lu Y. Informative gene selection for microarray classification via adaptive elastic net with conditional mutual information. Appl Math Model. 2019;71:286\u201397.","journal-title":"Appl Math Model"},{"key":"1344_CR41","doi-asserted-by":"publisher","first-page":"179","DOI":"10.1016\/j.apm.2021.12.016","volume":"105","author":"Y Wang","year":"2022","unstructured":"Wang Y, Zhang W, Fan M, et al. Regression with adaptive lasso and correlation based penalty. Appl Math Model. 2022;105:179\u201396.","journal-title":"Appl Math Model"},{"key":"1344_CR42","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2024.124502","author":"Y Wang","year":"2024","unstructured":"Wang Y, Huang M, Zhou L, et al. Multi-cluster nonlinear unsupervised feature selection via joint manifold learning and generalized lasso. Expert Syst Appl. 2024. https:\/\/doi.org\/10.1016\/j.eswa.2024.124502.","journal-title":"Expert Syst Appl"},{"key":"1344_CR43","doi-asserted-by":"publisher","first-page":"193","DOI":"10.1016\/j.ins.2022.03.037","volume":"597","author":"L Xu","year":"2022","unstructured":"Xu L, Chen CP, Han R. Graph-based sparse Bayesian broad learning system for semi-supervised learning. Inf Sci. 2022;597:193\u2013210.","journal-title":"Inf Sci"},{"issue":"11","key":"1344_CR44","doi-asserted-by":"publisher","first-page":"5056","DOI":"10.1109\/TKDE.2021.3059523","volume":"34","author":"X Xu","year":"2021","unstructured":"Xu X, Wu X, Wei F, et al. A general framework for feature selection under orthogonal regression with global redundancy minimization. IEEE Trans Knowl Data Eng. 2021;34(11):5056\u201369.","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"1344_CR45","unstructured":"Yang Y, Shen HT, Ma Z, et\u00a0al. $$\\ell$$ 2, 1-norm regularized discriminative feature selection for unsupervised learning. In: IJCAI international joint conference on artificial intelligence; 2011."},{"key":"1344_CR46","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2024.108108","volume":"133","author":"T Yin","year":"2024","unstructured":"Yin T, Chen H, Yuan Z, et al. LEFMIFS: label enhancement and fuzzy mutual information for robust multilabel feature selection. Eng Appl Artif Intell. 2024;133:108108.","journal-title":"Eng Appl Artif Intell"},{"key":"1344_CR47","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2023.107168","volume":"126","author":"G Yuan","year":"2023","unstructured":"Yuan G, Lu L, Zhou X. Feature selection using a sinusoidal sequence combined with mutual information. Eng Appl Artif Intell. 2023;126:107168.","journal-title":"Eng Appl Artif Intell"},{"issue":"1","key":"1344_CR48","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1111\/j.1467-9868.2005.00532.x","volume":"68","author":"M Yuan","year":"2006","unstructured":"Yuan M, Lin Y. Model selection and estimation in regression with grouped variables. J R Stat Soc Ser B (Stat Methodol). 2006;68(1):49\u201367.","journal-title":"J R Stat Soc Ser B (Stat Methodol)"},{"issue":"4","key":"1344_CR49","doi-asserted-by":"publisher","first-page":"659","DOI":"10.1109\/TKDE.2019.2893266","volume":"32","author":"H Zhang","year":"2020","unstructured":"Zhang H, Wang J, Sun Z, et al. Feature selection for neural networks using group lasso regularization. IEEE Trans Knowl Data Eng. 2020;32(4):659\u201373.","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"5","key":"1344_CR50","doi-asserted-by":"publisher","first-page":"5457","DOI":"10.1007\/s10489-021-02524-x","volume":"52","author":"H Zhou","year":"2022","unstructured":"Zhou H, Wang X, Zhu R. Feature selection based on mutual information with correlation coefficient. Appl Intell. 2022;52(5):5457\u201374.","journal-title":"Appl Intell"},{"issue":"2","key":"1344_CR51","doi-asserted-by":"publisher","first-page":"301","DOI":"10.1111\/j.1467-9868.2005.00503.x","volume":"67","author":"H Zou","year":"2005","unstructured":"Zou H, Hastie T. Regularization and variable selection via the elastic net. J R Stat Soc Ser B Stat Methodol. 2005;67(2):301\u201320.","journal-title":"J R Stat Soc Ser B Stat Methodol"}],"container-title":["Journal of Big Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s40537-025-01344-x","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s40537-025-01344-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s40537-025-01344-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,2]],"date-time":"2026-02-02T14:04:01Z","timestamp":1770041041000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1186\/s40537-025-01344-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,31]]},"references-count":51,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["1344"],"URL":"https:\/\/doi.org\/10.1186\/s40537-025-01344-x","relation":{},"ISSN":["2196-1115"],"issn-type":[{"value":"2196-1115","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,12,31]]},"assertion":[{"value":"26 June 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 November 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"31 December 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":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"12"}}