{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,31]],"date-time":"2026-07-31T15:35:05Z","timestamp":1785512105595,"version":"3.56.0"},"reference-count":53,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2024,10,16]],"date-time":"2024-10-16T00:00:00Z","timestamp":1729036800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,10,16]],"date-time":"2024-10-16T00:00:00Z","timestamp":1729036800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62376114"],"award-info":[{"award-number":["62376114"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"the Nature Science Foundation of Fujian Province","award":["2021J011004"],"award-info":[{"award-number":["2021J011004"]}]},{"name":"the Nature Science Foundation of Fujian Province","award":["2021J011002"],"award-info":[{"award-number":["2021J011002"]}]},{"name":"the Ministry of Education Industry-University-Research Innovation Program","award":["2021LDA09003"],"award-info":[{"award-number":["2021LDA09003"]}]},{"name":"High level cultivation projects of Minnan Normal University","award":["MSGJB2021007"],"award-info":[{"award-number":["MSGJB2021007"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Knowl Inf Syst"],"published-print":{"date-parts":[[2025,2]]},"DOI":"10.1007\/s10115-024-02258-5","type":"journal-article","created":{"date-parts":[[2024,10,16]],"date-time":"2024-10-16T20:31:05Z","timestamp":1729110665000},"page":"1271-1308","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Semi-supervised feature selection with minimal redundancy based on group optimization strategy for multi-label data"],"prefix":"10.1007","volume":"67","author":[{"given":"Depeng","family":"Qing","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yifeng","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenjie","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weishuo","family":"Ren","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xianlong","family":"Zeng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guohe","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,10,16]]},"reference":[{"issue":"26","key":"2258_CR1","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1016\/j.neucom.2017.11.077","volume":"300","author":"J Cai","year":"2018","unstructured":"Cai J, Luo J, Wang S, Yang S (2018) Feature selection in machine learning: a new perspective. Neurocomputing 300(26):70\u201379","journal-title":"Neurocomputing"},{"issue":"12","key":"2258_CR2","volume":"214","author":"S Lv","year":"2021","unstructured":"Lv S, Shi S, Wang H, Li F (2021) Semi-supervised multi-label feature selection with adaptive structure learning and manifold learning. Knowl-Based Syst 214(12):106757","journal-title":"Knowl-Based Syst"},{"issue":"11","key":"2258_CR3","doi-asserted-by":"crossref","first-page":"324","DOI":"10.3390\/a14110324","volume":"14","author":"X Wang","year":"2021","unstructured":"Wang X (2021) Feature selection for high-dimensional datasets through a novel artificial bee colony framework. Algorithms 14(11):324\u2013343","journal-title":"Algorithms"},{"issue":"3","key":"2258_CR4","doi-asserted-by":"crossref","first-page":"993","DOI":"10.1016\/j.ejor.2017.08.040","volume":"265","author":"B Ghaddar","year":"2018","unstructured":"Ghaddar B, Naoum-Sawaya J (2018) High dimensional data classification and feature selection using support vector machines. Eur J Oper Res 265(3):993\u20131004","journal-title":"Eur J Oper Res"},{"issue":"5","key":"2258_CR5","doi-asserted-by":"crossref","first-page":"12553","DOI":"10.1111\/exsy.12553","volume":"37","author":"C Chen","year":"2020","unstructured":"Chen C, Tsai Y, Chang F, Lin W (2020) Ensemble feature selection in medical datasets: combining filter, wrapper, and embedded feature selection results. Expert Syst 37(5):12553","journal-title":"Expert Syst"},{"issue":"4","key":"2258_CR6","doi-asserted-by":"crossref","first-page":"685","DOI":"10.34768\/amcs-2021-0047","volume":"31","author":"M Kusy","year":"2021","unstructured":"Kusy M, Zajdel R (2021) A weighted wrapper approach to feature selection. Int J Appl Math Comput Sci 31(4):685\u2013696","journal-title":"Int J Appl Math Comput Sci"},{"issue":"10","key":"2258_CR7","doi-asserted-by":"crossref","first-page":"1640","DOI":"10.1360\/SSI-2020-0055","volume":"51","author":"RH Shang","year":"2021","unstructured":"Shang RH, Xu KM, Jiao LC (2021) Adaptive dual graphs and non-convex constraint based embedded feature selection (in Chinese). Sci Sin Inform 51(10):1640\u20131657","journal-title":"Sci Sin Inform"},{"key":"2258_CR8","unstructured":"Hopf K, Reifenrath S (2021) Filter methods for feature selection in supervised machine learning applications\u2013review and benchmark. CoRR abs\/2111.12140, 1\u201338"},{"key":"2258_CR9","doi-asserted-by":"crossref","first-page":"210","DOI":"10.1016\/j.knosys.2015.06.008","volume":"86","author":"Z Sun","year":"2015","unstructured":"Sun Z, Han J, Hongwei H (2015) Selecting feature subset with sparsity and low redundancy for unsupervised learning. Knowl-Based Syst 86:210\u2013223","journal-title":"Knowl-Based Syst"},{"key":"2258_CR10","doi-asserted-by":"crossref","first-page":"108622","DOI":"10.1016\/j.patcog.2022.108622","volume":"127","author":"P Huang","year":"2022","unstructured":"Huang P, Yang X (2022) Unsupervised feature selection via adaptive graph and dependency score. Pattern Recognit. 127:108622\u2013108635","journal-title":"Pattern Recognit."},{"issue":"3","key":"2258_CR11","doi-asserted-by":"crossref","first-page":"1955","DOI":"10.1007\/s10115-023-01993-5","volume":"66","author":"F Rahmat","year":"2024","unstructured":"Rahmat F, Zulkafli Z, Ishak AJ, Abdul Rahman RZ, Stercke SD, Buytaert W, Tahir W, Ab Rahman J, Ibrahim S, Ismail M (2024) Supervised feature selection using principal component analysis. Knowl Inf Syst 66(3):1955\u20131995","journal-title":"Knowl Inf Syst"},{"issue":"5","key":"2258_CR12","doi-asserted-by":"crossref","first-page":"4077","DOI":"10.1007\/s10462-022-10274-6","volume":"56","author":"J Jiang","year":"2023","unstructured":"Jiang J, Zhang X, Yang J (2023) Unsupervised feature selection based on incremental forward iterative Laplacian score. Artif Intell Rev 56(5):4077\u20134112","journal-title":"Artif Intell Rev"},{"key":"2258_CR13","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1016\/j.jbi.2018.07.014","volume":"85","author":"RJ Urbanowicz","year":"2018","unstructured":"Urbanowicz RJ, Meeker M, Cava WGL, Olson RS, Moore JH (2018) Relief-based feature selection: introduction and review. J Biomed Inform 85:189\u2013203","journal-title":"J Biomed Inform"},{"key":"2258_CR14","doi-asserted-by":"crossref","first-page":"887","DOI":"10.1016\/j.ins.2021.08.032","volume":"5788","author":"L Sun","year":"2021","unstructured":"Sun L, Wang T, Ding W (2021) Feature selection using Fisher score and multilabel neighborhood rough sets for multilabel classification. Inf Sci 5788:887\u2013912","journal-title":"Inf Sci"},{"issue":"6","key":"2258_CR15","first-page":"1279","volume":"16","author":"Y Liu","year":"2022","unstructured":"Liu Y, Zheng Y, Jiang L, Li G, Zhang W (2022) Survey on pseudo-labeling methods in deep semi-supervised learning. J Front Comput Sci Technol 16(6):1279\u20131290","journal-title":"J Front Comput Sci Technol"},{"key":"2258_CR16","doi-asserted-by":"crossref","unstructured":"Li Z, Ko B, Choi H (2018) Pseudo-labeling using gaussian process for semi-supervised deep learning. In: 2018 IEEE international conference on big data and smart computing, BigComp 2018, Shanghai, China, January 15\u201317, 2018, pp 263\u2013269","DOI":"10.1109\/BigComp.2018.00046"},{"key":"2258_CR17","doi-asserted-by":"crossref","unstructured":"Guo B, Hou C, Nie F, Yi D (2016) Semi-supervised multi-label dimensionality reduction. In: 2016 IEEE 16th international conference on data mining (ICDM), pp 919\u2013924","DOI":"10.1109\/ICDM.2016.0113"},{"key":"2258_CR18","doi-asserted-by":"crossref","unstructured":"Fang S-G, Huang D, Wang C-D, Tang Y (2023) Joint multi-view unsupervised feature selection and graph learning. IEEE Trans Emerging Top Comput Intell, 1\u201318","DOI":"10.1109\/TETCI.2023.3306233"},{"key":"2258_CR19","volume":"294","author":"R Li","year":"2024","unstructured":"Li R, Zhou G et al (2024) Semi-supervised multi-label dimensionality reduction learning based on minimizing redundant correlation of specific and common features. Knowl. Based Syst. 294:111789","journal-title":"Knowl. Based Syst."},{"issue":"8","key":"2258_CR20","doi-asserted-by":"crossref","first-page":"1819","DOI":"10.1109\/TKDE.2013.39","volume":"26","author":"M Zhang","year":"2014","unstructured":"Zhang M, Zhou Z (2014) A review on multi-label learning algorithms. IEEE Trans Knowl Data Eng 26(8):1819\u20131837","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"1","key":"2258_CR21","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1007\/s10115-015-0841-8","volume":"47","author":"A Alalga","year":"2016","unstructured":"Alalga A, Benabdeslem K, Taleb N (2016) Soft-constrained Laplacian score for semi-supervised multi-label feature selection. Knowl Inf Syst 47(1):75\u201398","journal-title":"Knowl Inf Syst"},{"key":"2258_CR22","unstructured":"L., Y.J., B., S.X., Z, H, (2022) Label-correlation-based common and specific feature selection for hierarchical classification. J Softw 33(7):2667\u20132682"},{"key":"2258_CR23","first-page":"1131","volume":"6","author":"J Zhu","year":"2009","unstructured":"Zhu J, Zheng J (2009) Theory, method and application of group decision-making: literature review and future directions. Chin J Manag 6:1131\u20131136","journal-title":"Chin J Manag"},{"key":"2258_CR24","doi-asserted-by":"crossref","first-page":"265","DOI":"10.1007\/s41019-016-0022-0","volume":"1","author":"MH Rehman","year":"2016","unstructured":"Rehman MH, Liew CS, Abbas A, Jayaraman PP, Wah TY, Khan SU (2016) Big data reduction methods: a survey. Data Sci Eng 1:265\u2013284","journal-title":"Data Sci Eng"},{"key":"2258_CR25","doi-asserted-by":"crossref","unstructured":"Zhou Z-H, Zhou Z-H (2021) Semi-supervised learning. Mach Learn, 315\u2013341","DOI":"10.1007\/978-981-15-1967-3_13"},{"key":"2258_CR26","first-page":"2456","volume":"24","author":"M Chen","year":"2011","unstructured":"Chen M, Weinberger KQ, Blitzer J (2011) Co-training for domain adaptation. Adv Neural Inf Process Syst 24:2456\u20132464","journal-title":"Adv Neural Inf Process Syst"},{"issue":"5","key":"2258_CR27","doi-asserted-by":"crossref","first-page":"1131","DOI":"10.1109\/TKDE.2013.86","volume":"26","author":"K Benabdeslem","year":"2014","unstructured":"Benabdeslem K, Hindawi M (2014) Efficient semi-supervised feature selection: constraint, relevance, and redundancy. IEEE Trans Knowl Data Eng 26(5):1131\u20131143","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"2258_CR28","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1016\/j.ins.2018.08.035","volume":"468","author":"S Razieh","year":"2018","unstructured":"Razieh S, Agha SM, Elnaz S (2018) Semi-supervised sparse feature selection via graph Laplacian based scatter matrix for regression problems. Inf Sci 468:14\u201328","journal-title":"Inf Sci"},{"key":"2258_CR29","doi-asserted-by":"crossref","first-page":"178","DOI":"10.1016\/j.ins.2021.02.035","volume":"566","author":"W Zhong","year":"2021","unstructured":"Zhong W, Chen X, Nie F, Huang JZ (2021) Adaptive discriminant analysis for semi-supervised feature selection. Inf Sci 566:178\u2013194","journal-title":"Inf Sci"},{"issue":"9","key":"2258_CR30","first-page":"123","volume":"64","author":"Z Li","year":"2021","unstructured":"Li Z, Tang J (2021) Semi-supervised local feature selection for data classification. Science China Inf Sci 64(9):123\u2013134","journal-title":"Science China Inf Sci"},{"key":"2258_CR31","doi-asserted-by":"crossref","first-page":"8542","DOI":"10.1007\/s10489-021-02288-4","volume":"51","author":"X Wu","year":"2021","unstructured":"Wu X, Chen H, Li T, Wan J (2021) Semi-supervised feature selection with minimal redundancy based on local adaptive. Appl Intell 51:8542\u20138563","journal-title":"Appl Intell"},{"key":"2258_CR32","doi-asserted-by":"crossref","DOI":"10.1016\/j.inffus.2023.101948","volume":"100","author":"W Qian","year":"2023","unstructured":"Qian W, Huang J, Xu F, Shu W, Ding W (2023) A survey on multi-label feature selection from perspectives of label fusion. Inf Fusion 100:101948","journal-title":"Inf Fusion"},{"key":"2258_CR33","doi-asserted-by":"crossref","unstructured":"Lian Z, Sun H, Sun L, et\u00a0al. (2023) Mer 2023: Multi-label learning, modality robustness, and semi-supervised learning. In: Proceedings of the 31st ACM international conference on multimedia, pp 9610\u20139614","DOI":"10.1145\/3581783.3612836"},{"issue":"7","key":"2258_CR34","doi-asserted-by":"crossref","first-page":"2038","DOI":"10.1016\/j.patcog.2006.12.019","volume":"40","author":"ML Zhang","year":"2007","unstructured":"Zhang ML, Zhou ZH (2007) ML-KNN: a lazy learning approach to multi-label learning. Pattern Recogn 40(7):2038\u20132048","journal-title":"Pattern Recogn"},{"key":"2258_CR35","doi-asserted-by":"crossref","first-page":"244","DOI":"10.1016\/j.asoc.2015.10.009","volume":"38","author":"Y Lin","year":"2016","unstructured":"Lin Y, Hu Q, Liu J, Chen J, Duan J (2016) Multi-label feature selection based on neighborhood mutual information. Appl Soft Comput 38:244\u2013256","journal-title":"Appl Soft Comput"},{"issue":"30","key":"2258_CR36","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1016\/j.neucom.2015.06.010","volume":"168","author":"Y Lin","year":"2015","unstructured":"Lin Y, Hu Q, Liu J, Duan J (2015) Multi-label feature selection based on max-dependency and min-redundancy. Neurocomputing 168(30):92\u2013103","journal-title":"Neurocomputing"},{"key":"2258_CR37","volume":"134","author":"LH Yonghao Li","year":"2023","unstructured":"Yonghao Li LH, Gao W (2023) Multi-label feature selection via robust flexible sparse regularization. Pattern Recognit 134:109074","journal-title":"Pattern Recognit"},{"key":"2258_CR38","first-page":"1627","volume":"16","author":"L Jian","year":"2016","unstructured":"Jian L, Li J, Shu K, Liu H (2016) Multi-label informed feature selection. IJCAI 16:1627\u201333","journal-title":"IJCAI"},{"key":"2258_CR39","doi-asserted-by":"crossref","first-page":"136","DOI":"10.1016\/j.patcog.2019.06.003","volume":"95","author":"J Zhang","year":"2019","unstructured":"Zhang J, Luo Z, Li C, Zhou C, Li S (2019) Manifold regularized discriminative feature selection for multi-label learning. Pattern Recogn 95:136\u2013150","journal-title":"Pattern Recogn"},{"key":"2258_CR40","first-page":"1","volume":"203","author":"J Hu","year":"2020","unstructured":"Hu J, Li Y, Gao W, Zhang P (2020) Robust multi-label feature selection with dual-graph regularization. Knowl-Based Syst 203:1\u201312","journal-title":"Knowl-Based Syst"},{"key":"2258_CR41","doi-asserted-by":"crossref","first-page":"9032","DOI":"10.1109\/ACCESS.2018.2890549","volume":"7","author":"Y Zheng","year":"2019","unstructured":"Zheng Y, Li G, Zhang W, Li Y, Wei B (2019) Feature selection with ensemble learning based on improved Dempster\u2013Shafer evidence fusion. IEEE Access 7:9032\u20139045","journal-title":"IEEE Access"},{"key":"2258_CR42","doi-asserted-by":"crossref","DOI":"10.1016\/j.cose.2024.103821","volume":"141","author":"M Zhong","year":"2024","unstructured":"Zhong M, Lin M, Zhang C, Xu Z (2024) A survey on graph neural networks for intrusion detection systems: methods, trends and challenges. Comput Secur 141:103821","journal-title":"Comput Secur"},{"key":"2258_CR43","unstructured":"Cheng S (2013) Research on multi-label clustering algorithms and their evaluation. PhD thesis, Northeast Normal University"},{"key":"2258_CR44","unstructured":"Lee D, Seung HS (2000) Algorithms for non-negative matrix factorization 13:1\u20137"},{"issue":"8","key":"2258_CR45","volume":"120","author":"R Huang","year":"2021","unstructured":"Huang R, Wu Z (2021) Multi-label feature selection via manifold regularization and dependence maximization. Pattern Recogn 120(8):108149","journal-title":"Pattern Recogn"},{"issue":"3","key":"2258_CR46","doi-asserted-by":"crossref","first-page":"349","DOI":"10.1016\/j.patrec.2012.10.005","volume":"34","author":"J Lee","year":"2013","unstructured":"Lee J, Kim DW (2013) Feature selection for multi-label classification using multivariate mutual information. Pattern Recogn Lett 34(3):349\u2013357","journal-title":"Pattern Recogn Lett"},{"key":"2258_CR47","doi-asserted-by":"crossref","first-page":"1321","DOI":"10.1007\/s13042-017-0647-y","volume":"9","author":"Z Cai","year":"2018","unstructured":"Cai Z, Zhu W (2018) Multi-label feature selection via feature manifold learning and sparsity regularization. Int J Mach Learn Cybern 9:1321\u20131334","journal-title":"Int J Mach Learn Cybern"},{"issue":"1","key":"2258_CR48","doi-asserted-by":"crossref","first-page":"346","DOI":"10.1016\/j.patrec.2018.08.021","volume":"112","author":"R Huang","year":"2018","unstructured":"Huang R, Jiang W, Sun G (2018) Manifold-based constraint Laplacian score for multi-label feature selection. Pattern Recognit Lett 112(1):346\u2013352","journal-title":"Pattern Recognit Lett"},{"key":"2258_CR49","doi-asserted-by":"crossref","unstructured":"Chen X, Yuan G, Nie F, Huang JZ (2017) Semi-supervised feature selection via rescaled linear regression. In: IJCAI, vol 2017, pp 1525\u20131531","DOI":"10.24963\/ijcai.2017\/211"},{"key":"2258_CR50","doi-asserted-by":"crossref","unstructured":"Zhang J, Lin Y, Jiang M, Li S, Tang Y, Tan KC (2020) Multi-label feature selection via global relevance and redundancy optimization. In: IJCAI, pp 2512\u20132518","DOI":"10.24963\/ijcai.2020\/348"},{"issue":"200","key":"2258_CR51","doi-asserted-by":"crossref","first-page":"675","DOI":"10.1080\/01621459.1937.10503522","volume":"32","author":"M Friedman","year":"1939","unstructured":"Friedman M (1939) The use of ranks to avoid the assumption of normality implicit in the analysis of variance. Publ Am Stat Assoc 32(200):675\u2013701","journal-title":"Publ Am Stat Assoc"},{"issue":"1","key":"2258_CR52","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1214\/aoms\/1177731944","volume":"11","author":"M Friedman","year":"1940","unstructured":"Friedman M (1940) A comparison of alternative tests of significance for the problem of $$m$$ rankings. Ann Math Stat 11(1):86\u201392","journal-title":"Ann Math Stat"},{"issue":"293","key":"2258_CR53","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1080\/01621459.1961.10482090","volume":"56","author":"OJ Dunn","year":"1961","unstructured":"Dunn OJ (1961) Multiple comparisons among means. Publ Am Stat Assoc 56(293):52\u201364","journal-title":"Publ Am Stat Assoc"}],"container-title":["Knowledge and Information Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10115-024-02258-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10115-024-02258-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10115-024-02258-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,31]],"date-time":"2025-01-31T13:49:55Z","timestamp":1738331395000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10115-024-02258-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,16]]},"references-count":53,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2025,2]]}},"alternative-id":["2258"],"URL":"https:\/\/doi.org\/10.1007\/s10115-024-02258-5","relation":{},"ISSN":["0219-1377","0219-3116"],"issn-type":[{"value":"0219-1377","type":"print"},{"value":"0219-3116","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,10,16]]},"assertion":[{"value":"28 January 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 August 2024","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 October 2024","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 October 2024","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declared that they have no conflict of interest in this work. We declare that we do not have any commercial or associative interest that represents a conflict of interest in connection with the work submitted.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}