{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,23]],"date-time":"2026-08-23T17:19:20Z","timestamp":1787505560880,"version":"build-2736575974"},"reference-count":50,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,8,1]],"date-time":"2024-08-01T00:00:00Z","timestamp":1722470400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2024,8,1]],"date-time":"2024-08-01T00:00:00Z","timestamp":1722470400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"name":"Universitat Polit\u00e8cnica de Catalunya, Spain","award":["ALECTORS-2023"],"award-info":[{"award-number":["ALECTORS-2023"]}]},{"name":"Universitat Polit\u00e8cnica de Catalunya, Spain","award":["ALECTORS-2023"],"award-info":[{"award-number":["ALECTORS-2023"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BioData Mining"],"DOI":"10.1186\/s13040-024-00378-w","type":"journal-article","created":{"date-parts":[[2024,8,1]],"date-time":"2024-08-01T07:35:30Z","timestamp":1722497730000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["Understanding predictions of drug profiles using explainable machine learning models"],"prefix":"10.1186","volume":"17","author":[{"given":"Caroline","family":"K\u00f6nig","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alfredo","family":"Vellido","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,8,1]]},"reference":[{"issue":"1","key":"378_CR1","doi-asserted-by":"publisher","first-page":"17","DOI":"10.2174\/1570163817666200316104404","volume":"18","author":"MR Keyvanpour","year":"2021","unstructured":"Keyvanpour MR, Shirzad MB. An analysis of QSAR research based on machine learning concepts. Curr Drug Discov Technol. 2021;18(1):17\u201330.","journal-title":"Curr Drug Discov Technol"},{"issue":"4","key":"378_CR2","doi-asserted-by":"publisher","first-page":"bbaa321","DOI":"10.1093\/bib\/bbaa321","volume":"22","author":"Z Wu","year":"2020","unstructured":"Wu Z, Zhu M, Kang Y, Leung ELH, Lei T, Shen C, et al. Do we need different machine learning algorithms for QSAR modeling? A comprehensive assessment of 16 machine learning algorithms on 14 QSAR data sets. Brief Bioinforma. 2020;22(4):bbaa321.","journal-title":"Brief Bioinforma."},{"issue":"2","key":"378_CR3","doi-asserted-by":"publisher","first-page":"529","DOI":"10.1016\/j.drudis.2021.09.013","volume":"27","author":"V Kumar","year":"2022","unstructured":"Kumar V, Faheem M, Lee KW, et al. A decade of machine learning-based predictive models for human pharmacokinetics: Advances and challenges. Drug Discov Today. 2022;27(2):529\u201337.","journal-title":"Drug Discov Today"},{"issue":"11","key":"378_CR4","doi-asserted-by":"publisher","first-page":"1033","DOI":"10.2174\/156802605774297038","volume":"5","author":"SK Balani","year":"2005","unstructured":"Balani SK, Miwa GT, Gan LS, Wu JT, Lee FW. Strategy of utilizing in vitro and in vivo ADME tools for lead optimization and drug candidate selection. Curr Top Med Chem. 2005;5(11):1033\u20138.","journal-title":"Curr Top Med Chem"},{"issue":"16","key":"378_CR5","doi-asserted-by":"publisher","first-page":"8835","DOI":"10.1021\/acs.jmedchem.9b02187","volume":"63","author":"EN Feinberg","year":"2020","unstructured":"Feinberg EN, Joshi E, Pande VS, Cheng AC. Improvement in ADMET prediction with multitask deep featurization. J Med Chem. 2020;63(16):8835\u201348.","journal-title":"J Med Chem."},{"issue":"10","key":"378_CR6","doi-asserted-by":"publisher","first-page":"1033","DOI":"10.1038\/s41589-022-01131-2","volume":"18","author":"K Huang","year":"2022","unstructured":"Huang K, Fu T, Gao W, Zhao Y, Roohani Y, Leskovec J, et al. Artificial intelligence foundation for therapeutic science. Nat Chem Biol. 2022;18(10):1033\u20136.","journal-title":"Nat Chem Biol"},{"issue":"11","key":"378_CR7","doi-asserted-by":"publisher","first-page":"3263","DOI":"10.1021\/acs.jcim.3c00160","volume":"63","author":"C Fang","year":"2023","unstructured":"Fang C, Wang Y, Grater R, Kapadnis S, Black C, Trapa P, et al. Prospective Validation of Machine Learning Algorithms for Absorption, Distribution, Metabolism, and Excretion Prediction: An Industrial Perspective. J Chem Inf Model. 2023;63(11):3263\u201374.","journal-title":"J Chem Inf Model"},{"issue":"2","key":"378_CR8","doi-asserted-by":"publisher","first-page":"169","DOI":"10.1080\/17425255.2017.1316449","volume":"14","author":"K Przybylak","year":"2018","unstructured":"Przybylak K, Madden J, Covey-Crump E, Gibson L, Barber C, Patel M, et al. Characterisation of data resources for in silico modelling: benchmark datasets for ADME properties. Expert Opin Drug Metab Toxicol. 2018;14(2):169\u201381.","journal-title":"Expert Opin Drug Metab Toxicol."},{"issue":"2","key":"378_CR9","doi-asserted-by":"publisher","first-page":"513","DOI":"10.1039\/C7SC02664A","volume":"9","author":"Z Wu","year":"2018","unstructured":"Wu Z, Ramsundar B, Feinberg EN, Gomes J, Geniesse C, Pappu AS, et al. MoleculeNet: a benchmark for molecular machine learning. Chem Sci. 2018;9(2):513\u201330.","journal-title":"Chem Sci"},{"key":"378_CR10","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.ddtec.2020.11.009","volume":"37","author":"O Wieder","year":"2020","unstructured":"Wieder O, Kohlbacher S, Kuenemann M, Garon A, Ducrot P, Seidel T, et al. A compact review of molecular property prediction with graph neural networks. Drug Discov Today Technol. 2020;37:1\u201312.","journal-title":"Drug Discov Today Technol"},{"key":"378_CR11","unstructured":"Gilmer J, Schoenholz SS, Riley PF, Vinyals O, Dahl GE. Neural message passing for quantum chemistry. In: International conference on machine learning.\u00a0Sydney: JMLR.org; 2017. p. 1263\u201372."},{"issue":"2","key":"378_CR12","doi-asserted-by":"publisher","first-page":"263","DOI":"10.1021\/ci500747n","volume":"55","author":"J Ma","year":"2015","unstructured":"Ma J, Sheridan RP, Liaw A, Dahl GE, Svetnik V. Deep neural nets as a method for quantitative structure-activity relationships. J Chem Inf Model. 2015;55(2):263\u201374.","journal-title":"J Chem Inf Model"},{"issue":"2","key":"378_CR13","doi-asserted-by":"publisher","first-page":"141","DOI":"10.1038\/s41573-023-00832-0","volume":"23","author":"A Tropsha","year":"2024","unstructured":"Tropsha A, Isayev O, Varnek A, Schneider G, Cherkasov A. Integrating QSAR modelling and deep learning in drug discovery: the emergence of deep QSAR. Nat Rev Drug Discov. 2024;23(2):141\u201355.","journal-title":"Nat Rev Drug Discov"},{"key":"378_CR14","doi-asserted-by":"publisher","first-page":"42200","DOI":"10.1109\/ACCESS.2020.2976199","volume":"8","author":"R Roscher","year":"2020","unstructured":"Roscher R, Bohn B, Duarte MF, Garcke J. Explainable machine learning for scientific insights and discoveries. IEEE Access. 2020;8:42200\u201316.","journal-title":"IEEE Access."},{"key":"378_CR15","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1016\/j.neucom.2023.02.040","volume":"535","author":"P Lisboa","year":"2023","unstructured":"Lisboa P, Saralajew S, Vellido A, Fern\u00e1ndez-Domenech R, Villmann T. The coming of age of interpretable and explainable machine learning models. Neurocomputing. 2023;535:25\u201339.","journal-title":"Neurocomputing"},{"key":"378_CR16","doi-asserted-by":"publisher","first-page":"245","DOI":"10.1613\/jair.1.12228","volume":"70","author":"N Burkart","year":"2021","unstructured":"Burkart N, Huber MF. A survey on the explainability of supervised machine learning. J Artif Intell Res. 2021;70:245\u2013317.","journal-title":"J Artif Intell Res."},{"key":"378_CR17","doi-asserted-by":"publisher","first-page":"1461","DOI":"10.1007\/s11030-021-10266-8","volume":"25","author":"V Gallego","year":"2021","unstructured":"Gallego V, Naveiro R, Roca C, Rios Insua D, Campillo NE. AI in drug development: a multidisciplinary perspective. Mol Divers. 2021;25:1461\u201379.","journal-title":"Mol Divers"},{"key":"378_CR18","doi-asserted-by":"publisher","unstructured":"Yang G, Rao A, Fernandez-Maloigne C, Calhoun V, Menegaz G. Explainable AI (XAI) in biomedical signal and image processing: promises and challenges.\u00a0 In: 2022 IEEE International Conference on Image Processing (ICIP). Bordeaux: IEEE; 2022. p. 1531\u20131535. https:\/\/doi.org\/10.1109\/ICIP46576.2022.9897629.","DOI":"10.1109\/ICIP46576.2022.9897629"},{"key":"378_CR19","doi-asserted-by":"crossref","unstructured":"Ribeiro MT, Singh S, Guestrin C. \u201cWhy should i trust you?\u201d Explaining the predictions of any classifier. In: Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining.\u00a0New York:\u00a0Association for Computing Machinery; 2016. p. 1135\u201344.","DOI":"10.1145\/2939672.2939778"},{"key":"378_CR20","unstructured":"Lundberg SM, Lee SI. A unified approach to interpreting model predictions. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS\u201917.\u00a0Red Hook: Curran Associates Inc.; 2017. p. 4768\u201377."},{"key":"378_CR21","doi-asserted-by":"publisher","first-page":"1189","DOI":"10.1214\/aos\/1013203451","volume":"29","author":"JH Friedman","year":"2001","unstructured":"Friedman JH. Greedy function approximation: a gradient boosting machine. Ann Stat. 2001;29:1189\u2013232.","journal-title":"Ann Stat"},{"issue":"16","key":"378_CR22","doi-asserted-by":"publisher","first-page":"8761","DOI":"10.1021\/acs.jmedchem.9b01101","volume":"63","author":"R Rodr\u00edguez-P\u00e9rez","year":"2019","unstructured":"Rodr\u00edguez-P\u00e9rez R, Bajorath J. Interpretation of Compound Activity Predictions from Complex Machine Learning Models using Local Approximations and Shapley Values. J Med Chem. 2019;63(16):8761\u201377.","journal-title":"J Med Chem"},{"key":"378_CR23","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13321-021-00542-y","volume":"13","author":"A Wojtuch","year":"2021","unstructured":"Wojtuch A, Jankowski R, Podlewska S. How can SHAP values help to shape metabolic stability of chemical compounds? J Cheminformatics. 2021;13:1\u201320.","journal-title":"J Cheminformatics."},{"key":"378_CR24","doi-asserted-by":"crossref","unstructured":"Anjum M, Khan K, Ahmad W, Ahmad A, Amin MN, Nafees A. New SHapley Additive ExPlanations (SHAP) Approach to Evaluate the Raw Materials Interactions of Steel-Fiber-Reinforced Concrete. Materials. 2022;15(18):6261.","DOI":"10.3390\/ma15186261"},{"issue":"1","key":"378_CR25","doi-asserted-by":"publisher","first-page":"55","DOI":"10.1021\/cc9800071","volume":"1","author":"AK Ghose","year":"1999","unstructured":"Ghose AK, Viswanadhan VN, Wendoloski JJ. A knowledge-based approach in designing combinatorial or medicinal chemistry libraries for drug discovery. 1. A qualitative and quantitative characterization of known drug databases. J Comb Chem. 1999;1(1):55\u20138.","journal-title":"J Comb Chem."},{"issue":"12","key":"378_CR26","doi-asserted-by":"publisher","first-page":"2615","DOI":"10.1021\/jm020017n","volume":"45","author":"DF Veber","year":"2002","unstructured":"Veber DF, Johnson SR, Cheng HY, Smith BR, Ward KW, Kopple KD. Molecular properties that influence the oral bioavailability of drug candidates. J Med Chem. 2002;45(12):2615\u201323.","journal-title":"J Med Chem"},{"key":"378_CR27","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman L. Random Forests. Mach Learn. 2001;45:5\u201332.","journal-title":"Mach Learn"},{"key":"378_CR28","unstructured":"Ke G, Meng Q, Finley T, Wang T, Chen W, Ma W, et\u00a0al. LightGBM: A highly efficient gradient boosting decision tree. Adv Neural Inf Process Syst. 2017;30:52."},{"key":"378_CR29","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1016\/S0169-7161(04)24011-1","volume":"24","author":"CD Sutton","year":"2005","unstructured":"Sutton CD. Classification and Regression Trees, Bagging, and Boosting. Handb Statist. 2005;24:303\u201329.","journal-title":"Handb Statist"},{"issue":"10","key":"378_CR30","doi-asserted-by":"publisher","first-page":"1340","DOI":"10.1093\/bioinformatics\/btq134","volume":"26","author":"A Altmann","year":"2010","unstructured":"Altmann A, Tolo\u015fi L, Sander O, Lengauer T. Permutation importance: a corrected feature importance measure. Bioinformatics. 2010;26(10):1340\u20137.","journal-title":"Bioinformatics"},{"key":"378_CR31","doi-asserted-by":"publisher","first-page":"647","DOI":"10.1007\/s10115-013-0679-x","volume":"41","author":"E \u0160trumbelj","year":"2014","unstructured":"\u0160trumbelj E, Kononenko I. Explaining prediction models and individual predictions with feature contributions. Knowl Inf Syst. 2014;41:647\u201365.","journal-title":"Knowl Inf Syst"},{"key":"378_CR32","doi-asserted-by":"publisher","unstructured":"Shapley LS.\u00a0A Value for N-Person Games.\u00a0Santa Monica:\u00a0RAND Corporation;\u00a01952. p. 295. https:\/\/doi.org\/10.7249\/P0295.","DOI":"10.7249\/P0295"},{"issue":"5","key":"378_CR33","doi-asserted-by":"publisher","first-page":"868","DOI":"10.1021\/ci990307l","volume":"39","author":"SA Wildman","year":"1999","unstructured":"Wildman SA, Crippen GM. Prediction of Physicochemical Parameters by Atomic Contributions. J Chem Inf Comput Sci. 1999;39(5):868\u201373.","journal-title":"J Chem Inf Comput Sci"},{"issue":"20","key":"378_CR34","doi-asserted-by":"publisher","first-page":"3714","DOI":"10.1021\/jm000942e","volume":"43","author":"P Ertl","year":"2000","unstructured":"Ertl P, Rohde B, Selzer P. Fast Calculation of Molecular Polar Surface Area as a Sum of Fragment-Based Contributions and Its Application to the Prediction of Drug Transport Properties. J Med Chem. 2000;43(20):3714\u20137.","journal-title":"J Med Chem"},{"issue":"4\u20135","key":"378_CR35","doi-asserted-by":"publisher","first-page":"464","DOI":"10.1016\/S1093-3263(00)00068-1","volume":"18","author":"P Labute","year":"2000","unstructured":"Labute P. A widely applicable set of descriptors. J Mol Graph Model. 2000;18(4\u20135):464\u201377.","journal-title":"J Mol Graph Model"},{"key":"378_CR36","doi-asserted-by":"crossref","unstructured":"Hall LH, Kier LB. The Molecular Connectivity Chi Indexes and Kappa Shape Indexes in Structure-Property Modeling. Rev Comput Chem. 1991;2:367\u2013422.","DOI":"10.1002\/9780470125793.ch9"},{"issue":"5","key":"378_CR37","doi-asserted-by":"publisher","first-page":"742","DOI":"10.1021\/ci100050t","volume":"50","author":"D Rogers","year":"2010","unstructured":"Rogers D, Hahn M. Extended-Connectivity Fingerprints. J Chem Inf Model. 2010;50(5):742\u201354.","journal-title":"J Chem Inf Model"},{"issue":"2","key":"378_CR38","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1021\/c160017a018","volume":"5","author":"HL Morgan","year":"1965","unstructured":"Morgan HL. The Generation of a Unique Machine Description for Chemical Structures-A Technique Developed at Chemical Abstracts Service. J Chem Doc. 1965;5(2):107\u201313.","journal-title":"J Chem Doc"},{"issue":"1","key":"378_CR39","first-page":"36","volume":"4","author":"R Obach","year":"2001","unstructured":"Obach R. The prediction of human clearance from hepatic microsomal metabolism data. Curr Opin Drug Discov Dev. 2001;4(1):36\u201344.","journal-title":"Curr Opin Drug Discov Dev."},{"issue":"4","key":"378_CR40","doi-asserted-by":"publisher","first-page":"367","DOI":"10.1016\/j.cbpa.2006.06.015","volume":"10","author":"MV Varma","year":"2006","unstructured":"Varma MV, Perumal OP, Panchagnula R. Functional role of P-glycoprotein in limiting peroral drug absorption: optimizing drug delivery. Curr Opin Chem Biol. 2006;10(4):367\u201373.","journal-title":"Curr Opin Chem Biol"},{"issue":"1","key":"378_CR41","doi-asserted-by":"publisher","first-page":"95","DOI":"10.1016\/j.addr.2011.12.008","volume":"64","author":"F Broccatelli","year":"2012","unstructured":"Broccatelli F, Larregieu CA, Cruciani G, Oprea TI, Benet LZ. Improving the prediction of the brain disposition for orally administered drugs using BDDCS. Adv Drug Deliv Rev. 2012;64(1):95\u2013109.","journal-title":"Adv Drug Deliv Rev"},{"issue":"1","key":"378_CR42","doi-asserted-by":"publisher","first-page":"e00932","DOI":"10.1002\/prp2.932","volume":"10","author":"L Jiang","year":"2022","unstructured":"Jiang L, Kumar S, Nuechterlein M, Reyes M, Tran D, Cabebe C, et al. Application of a high-resolution in vitro human MDR1-MDCK assay and in vivo studies in preclinical species to improve prediction of CNS drug penetration. Pharmacol Res Perspect. 2022;10(1):e00932.","journal-title":"Pharmacol Res Perspect."},{"issue":"14","key":"378_CR43","doi-asserted-by":"publisher","first-page":"3110","DOI":"10.1016\/j.bmc.2019.05.037","volume":"27","author":"H Sun","year":"2019","unstructured":"Sun H, Shah P, Nguyen K, Yu KR, Kerns E, Kabir M, et al. Predictive models of aqueous solubility of organic compounds built on A large dataset of high integrity. Bioorg Med Chem. 2019;27(14):3110\u20134.","journal-title":"Bioorg Med Chem."},{"issue":"2","key":"378_CR44","doi-asserted-by":"publisher","first-page":"229","DOI":"10.1021\/ci100364a","volume":"51","author":"T Cheng","year":"2011","unstructured":"Cheng T, Li Q, Wang Y, Bryant SH. Binary classification of aqueous solubility using support vector machines with reduction and recombination feature selection. J Chem Inf Model. 2011;51(2):229\u201336.","journal-title":"J Chem Inf Model"},{"issue":"1\u20133","key":"378_CR45","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1016\/S0169-409X(96)00423-1","volume":"23","author":"CA Lipinski","year":"1997","unstructured":"Lipinski CA, Lombardo F, Dominy BW, Feeney PJ. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv Drug Deliv Rev. 1997;23(1\u20133):3\u201325.","journal-title":"Adv Drug Deliv Rev"},{"key":"378_CR46","doi-asserted-by":"crossref","unstructured":"M\u00a0Honorio K, L\u00a0Moda T, D\u00a0Andricopulo A. Pharmacokinetic properties and in silico ADME modeling in drug discovery. Med Chem. 2013;9(2):163\u2013176.","DOI":"10.2174\/1573406411309020002"},{"key":"378_CR47","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1016\/j.chemolab.2017.09.005","volume":"170","author":"NN Wang","year":"2017","unstructured":"Wang NN, Deng ZK, Huang C, Dong J, Zhu MF, Yao ZJ, et al. ADME properties evaluation in drug discovery: Prediction of plasma protein binding using NSGA-II combining PLS and consensus modeling. Chemom Intell Lab Syst. 2017;170:84\u201395.","journal-title":"Chemom Intell Lab Syst"},{"key":"378_CR48","doi-asserted-by":"publisher","first-page":"136669","DOI":"10.1016\/j.cej.2022.136669","volume":"444","author":"AS Alshehri","year":"2022","unstructured":"Alshehri AS, You F. Deep learning to catalyze inverse molecular design. Chem Eng J. 2022;444:136669.","journal-title":"Chem Eng J"},{"issue":"35","key":"378_CR49","doi-asserted-by":"publisher","first-page":"5316","DOI":"10.1039\/D1CC07035E","volume":"58","author":"B Sridharan","year":"2022","unstructured":"Sridharan B, Goel M, Priyakumar UD. Modern machine learning for tackling inverse problems in chemistry: molecular design to realization. Chem Commun. 2022;58(35):5316\u201331.","journal-title":"Chem Commun"},{"issue":"2","key":"378_CR50","doi-asserted-by":"publisher","first-page":"333","DOI":"10.1021\/acs.jpca.1c08191","volume":"126","author":"NC Iovanac","year":"2022","unstructured":"Iovanac NC, MacKnight R, Savoie BM. Actively Searching: Inverse Design of Novel Molecules with Simultaneously Optimized Properties. J Phys Chem A. 2022;126(2):333\u201340.","journal-title":"J Phys Chem A"}],"container-title":["BioData Mining"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13040-024-00378-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s13040-024-00378-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13040-024-00378-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,1]],"date-time":"2024-08-01T07:37:21Z","timestamp":1722497841000},"score":1,"resource":{"primary":{"URL":"https:\/\/biodatamining.biomedcentral.com\/articles\/10.1186\/s13040-024-00378-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,1]]},"references-count":50,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["378"],"URL":"https:\/\/doi.org\/10.1186\/s13040-024-00378-w","relation":{"has-preprint":[{"id-type":"doi","id":"10.21203\/rs.3.rs-4478926\/v1","asserted-by":"object"}]},"ISSN":["1756-0381"],"issn-type":[{"value":"1756-0381","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,8,1]]},"assertion":[{"value":"26 May 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 July 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 August 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"All authors approved the manuscript and agreed with its publication.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"25"}}