{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,11]],"date-time":"2026-02-11T12:34:02Z","timestamp":1770813242985,"version":"3.50.1"},"reference-count":47,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2026,2,11]],"date-time":"2026-02-11T00:00:00Z","timestamp":1770768000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,2,11]],"date-time":"2026-02-11T00:00:00Z","timestamp":1770768000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Supercomput"],"DOI":"10.1007\/s11227-026-08303-0","type":"journal-article","created":{"date-parts":[[2026,2,11]],"date-time":"2026-02-11T06:38:52Z","timestamp":1770791932000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["MVCL: a multi-view contrastive learning framework for biomedical knowledge graphs"],"prefix":"10.1007","volume":"82","author":[{"given":"Nasrin","family":"Mazaheri Soudani","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ali","family":"Karami","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,2,11]]},"reference":[{"key":"8303_CR1","doi-asserted-by":"publisher","first-page":"26726","DOI":"10.7554\/eLife.26726","volume":"6","author":"DS Himmelstein","year":"2017","unstructured":"Himmelstein DS, Lizee A, Hessler C, Brueggeman L, Chen SL, Hadley D, Green A, Khankhanian P, Baranzini SE (2017) Systematic integration of biomedical knowledge prioritizes drugs for repurposing. Elife 6:26726. https:\/\/doi.org\/10.7554\/eLife.26726","journal-title":"Elife"},{"issue":"12","key":"8303_CR2","doi-asserted-by":"publisher","first-page":"2724","DOI":"10.1109\/TKDE.2017.2754499","volume":"29","author":"Q Wang","year":"2017","unstructured":"Wang Q, Mao Z, Wang B, Guo L (2017) Knowledge graph embedding: a survey of approaches and applications. IEEE Trans Knowl Data Eng 29(12):2724\u20132743. https:\/\/doi.org\/10.1109\/TKDE.2017.2754499","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"1","key":"8303_CR3","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1109\/TNNLS.2020.2978386","volume":"32","author":"Z Wu","year":"2020","unstructured":"Wu Z, Pan S, Chen F, Long G, Zhang C, Yu PS (2020) A comprehensive survey on graph neural networks. IEEE Trans Neural Netw Learn Syst 32(1):4\u201324. https:\/\/doi.org\/10.1109\/TNNLS.2020.2978386","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"8303_CR4","doi-asserted-by":"publisher","first-page":"57","DOI":"10.1016\/j.aiopen.2021.01.001","volume":"1","author":"J Zhou","year":"2020","unstructured":"Zhou J, Cui G, Hu S, Zhang Z, Yang C, Liu Z, Wang L, Li C, Sun M (2020) Graph neural networks: a review of methods and applications. AI Open 1:57\u201381. https:\/\/doi.org\/10.1016\/j.aiopen.2021.01.001","journal-title":"AI Open"},{"key":"8303_CR5","doi-asserted-by":"publisher","unstructured":"Wang X, Ji H, Shi C, Wang B, Ye Y, Cui P, Yu PS (2019) Heterogeneous graph attention network. In: The World Wide Web Conference, pp. 2022\u20132032. https:\/\/doi.org\/10.1145\/3308558.3313562","DOI":"10.1145\/3308558.3313562"},{"key":"8303_CR6","doi-asserted-by":"publisher","unstructured":"Hamilton WL, Ying R, Leskovec J (2017) Representation learning on graphs: Methods and applications. https:\/\/doi.org\/10.48550\/arXiv.1709.05584","DOI":"10.48550\/arXiv.1709.05584"},{"key":"8303_CR7","unstructured":"Chen T, Kornblith S, Norouzi M, Hinton G (2020) A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597\u20131607 . PmLR. https:\/\/proceedings.mlr.press\/v119\/chen20j.html"},{"key":"8303_CR8","doi-asserted-by":"publisher","unstructured":"Oord AVD, Li Y, Vinyals O (2018) Representation learning with contrastive predictive coding. https:\/\/doi.org\/10.48550\/arXiv.1807.03748","DOI":"10.48550\/arXiv.1807.03748"},{"key":"8303_CR9","first-page":"5812","volume":"33","author":"Y You","year":"2020","unstructured":"You Y, Chen T, Sui Y, Chen T, Wang Z, Shen Y (2020) Graph contrastive learning with augmentations. Adv Neural Inf Process Syst 33:5812\u20135823","journal-title":"Adv Neural Inf Process Syst"},{"key":"8303_CR10","doi-asserted-by":"publisher","unstructured":"Zhu Y, Xu Y, Yu F, Liu Q, Wu S, Wang L (2020) Deep graph contrastive representation learning. https:\/\/doi.org\/10.48550\/arXiv.2006.04131","DOI":"10.48550\/arXiv.2006.04131"},{"key":"8303_CR11","unstructured":"Hassani K, Khasahmadi AH (2020) Contrastive multi-view representation learning on graphs. In: International Conference on Machine Learning, pp. 4116\u20134126. PMLR. https:\/\/proceedings.mlr.press\/v119\/hassani20a.html"},{"issue":"4","key":"8303_CR12","doi-asserted-by":"publisher","first-page":"1234","DOI":"10.1093\/bioinformatics\/btz682","volume":"36","author":"J Lee","year":"2020","unstructured":"Lee J, Yoon W, Kim S, Kim D, Kim S, So CH, Kang J (2020) Biobert: a pre-trained biomedical language representation model for biomedical text mining. Bioinformatics 36(4):1234\u20131240. https:\/\/doi.org\/10.1093\/bioinformatics\/btz682","journal-title":"Bioinformatics"},{"issue":"1","key":"8303_CR13","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1038\/75556","volume":"25","author":"M Ashburner","year":"2000","unstructured":"Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, Davis AP, Dolinski K, Dwight SS, Eppig JT et al (2000) Gene ontology: tool for the unification of biology. Nat Genet 25(1):25\u201329. https:\/\/doi.org\/10.1038\/75556","journal-title":"Nat Genet"},{"issue":"3","key":"8303_CR14","first-page":"265","volume":"88","author":"CE Lipscomb","year":"2000","unstructured":"Lipscomb CE (2000) Medical subject headings (mesh). Bull Med Libr Assoc 88(3):265","journal-title":"Bull Med Libr Assoc"},{"key":"8303_CR15","doi-asserted-by":"publisher","unstructured":"Zhang Q, Sun Z, Hu W, Chen M, Guo L, Qu Y (2019) Multi-view knowledge graph embedding for entity alignment. https:\/\/doi.org\/10.48550\/arXiv.1906.02390","DOI":"10.48550\/arXiv.1906.02390"},{"key":"8303_CR16","doi-asserted-by":"publisher","unstructured":"Sun Z, Hu W, Li C (2017) Cross-lingual entity alignment via joint attribute-preserving embedding. In: International Semantic Web Conference, pp. 628\u2013644. https:\/\/doi.org\/10.1007\/978-3-319-68288-4_37. Springer, New York","DOI":"10.1007\/978-3-319-68288-4_37"},{"issue":"4","key":"8303_CR17","doi-asserted-by":"publisher","first-page":"618","DOI":"10.1007\/s11227-025-07029-9","volume":"81","author":"M Lupo Pasini","year":"2025","unstructured":"Lupo Pasini M, Choi JY, Mehta K, Zhang P, Rogers D, Bae J, Ibrahim KZ, Aji AM, Schulz KW, Polo J et al (2025) Scalable training of trustworthy and energy-efficient predictive graph foundation models for atomistic materials modeling: a case study with hydragnn. J Supercomput 81(4):618. https:\/\/doi.org\/10.1007\/s11227-025-07029-9","journal-title":"J Supercomput"},{"issue":"11","key":"8303_CR18","doi-asserted-by":"publisher","first-page":"13455","DOI":"10.1007\/s11227-022-04399-2","volume":"78","author":"S Moreno-Alvarez","year":"2022","unstructured":"Moreno-Alvarez S, Paoletti ME, Rico-Gallego JA, Haut JM (2022) Heterogeneous gradient computing optimization for scalable deep neural networks. J Supercomput 78(11):13455\u201313469. https:\/\/doi.org\/10.1007\/s11227-022-04399-2","journal-title":"J Supercomput"},{"issue":"10","key":"8303_CR19","doi-asserted-by":"publisher","first-page":"10884","DOI":"10.1109\/TKDE.2023.3264691","volume":"35","author":"N Liu","year":"2023","unstructured":"Liu N, Wang X, Han H, Shi C (2023) Hierarchical contrastive learning enhanced heterogeneous graph neural network. IEEE Trans Knowl Data Eng 35(10):10884\u201310896. https:\/\/doi.org\/10.1109\/TKDE.2023.3264691","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"8303_CR20","doi-asserted-by":"publisher","unstructured":"Chen M, Huang C, Xia L, Wei W, Xu Y, Luo R (2023) Heterogeneous graph contrastive learning for recommendation. In: Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining, pp. 544\u2013552. https:\/\/doi.org\/10.1145\/3539597.3570484","DOI":"10.1145\/3539597.3570484"},{"key":"8303_CR21","doi-asserted-by":"publisher","unstructured":"Wang Z, Li Q, Yu D, Han X, Gao X-Z, Shen S (2023) Heterogeneous graph contrastive multi-view learning. In: Proceedings of the 2023 SIAM International Conference on Data Mining (SDM), pp. 136\u2013144. https:\/\/doi.org\/10.1137\/1.9781611977653.ch16 . SIAM","DOI":"10.1137\/1.9781611977653.ch16"},{"key":"8303_CR22","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2024.106645","volume":"181","author":"Y Sun","year":"2025","unstructured":"Sun Y, Zhu D, Wang Y, Fu Y, Tian Z (2025) GTC: GNN-transformer co-contrastive learning for self-supervised heterogeneous graph representation. Neural Netw 181:106645. https:\/\/doi.org\/10.1016\/j.neunet.2024.106645","journal-title":"Neural Netw"},{"issue":"9","key":"8303_CR23","doi-asserted-by":"publisher","first-page":"1912","DOI":"10.1093\/jamia\/ocae115","volume":"31","author":"H Ying","year":"2024","unstructured":"Ying H, Zhao Z, Zhao Y, Zeng S, Yu S (2024) Cortex: contrastive learning for representing terms via explanations with applications on constructing biomedical knowledge graphs. J Am Med Inform Assoc 31(9):1912\u20131920. https:\/\/doi.org\/10.1093\/jamia\/ocae115","journal-title":"J Am Med Inform Assoc"},{"issue":"1","key":"8303_CR24","doi-asserted-by":"publisher","first-page":"101","DOI":"10.1186\/s12915-025-02206-x","volume":"23","author":"Y Shang","year":"2025","unstructured":"Shang Y, Wang Z, Chen Y, Yang X, Ren Z, Zeng X, Xu L (2025) HNF-DDA: subgraph contrastive-driven transformer-style heterogeneous network embedding for drug-disease association prediction. BMC Biol 23(1):101. https:\/\/doi.org\/10.1186\/s12915-025-02206-x","journal-title":"BMC Biol"},{"key":"8303_CR25","doi-asserted-by":"publisher","unstructured":"Dang T, Nguyen VTD, Le MT, Hy T-S (2025) Multimodal contrastive representation learning in augmented biomedical knowledge graphs. https:\/\/doi.org\/10.48550\/arXiv.2501.01644","DOI":"10.48550\/arXiv.2501.01644"},{"issue":"12","key":"8303_CR26","doi-asserted-by":"publisher","first-page":"8682","DOI":"10.1109\/TKDE.2024.3471508","volume":"36","author":"T Ma","year":"2024","unstructured":"Ma T, Chen Y, Tao W, Zheng D, Lin X, Pang PC-I, Liu Y, Wang Y, Wang L, Song B et al (2024) Learning to denoise biomedical knowledge graph for robust molecular interaction prediction. IEEE Trans Knowl Data Eng 36(12):8682\u20138694. https:\/\/doi.org\/10.1109\/TKDE.2024.3471508","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"8303_CR27","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2025.122115","volume":"711","author":"X Liang","year":"2025","unstructured":"Liang X, Lai G, Yu J, Lin T, Wang C, Wang W (2025) Herbal ingredient-target interaction prediction via multi-modal learning. Inf Sci 711:122115. https:\/\/doi.org\/10.1016\/j.ins.2025.122115","journal-title":"Inf Sci"},{"issue":"1","key":"8303_CR28","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1186\/s12915-025-02128-8","volume":"23","author":"Y-A Huang","year":"2025","unstructured":"Huang Y-A, Li Y-C, You Z-H, Hu L, Hu P-W, Wang L, Peng Y, Huang Z-A (2025) Consensus representation of multiple cell-cell graphs from gene signaling pathways for cell type annotation. BMC Biol 23(1):23. https:\/\/doi.org\/10.1186\/s12915-025-02128-8","journal-title":"BMC Biol"},{"key":"8303_CR29","doi-asserted-by":"publisher","unstructured":"Hao J, Lei C, Efthymiou V, Quamar A, \u00d6zcan F, Sun Y, Wang W (2021) Medto: medical data to ontology matching using hybrid graph neural networks. In: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, pp. 2946\u20132954. https:\/\/doi.org\/10.1145\/3447548.3467138","DOI":"10.1145\/3447548.3467138"},{"issue":"1","key":"8303_CR30","doi-asserted-by":"publisher","first-page":"306","DOI":"10.1186\/s12859-019-2914-2","volume":"20","author":"Z Gao","year":"2019","unstructured":"Gao Z, Fu G, Ouyang C, Tsutsui S, Liu X, Yang J, Gessner C, Foote B, Wild D, Ding Y et al (2019) edge2vec: representation learning using edge semantics for biomedical knowledge discovery. BMC Bioinform 20(1):306. https:\/\/doi.org\/10.1186\/s12859-019-2914-2","journal-title":"BMC Bioinform"},{"key":"8303_CR31","doi-asserted-by":"publisher","unstructured":"Chang D, Bala\u017eevi\u0107 I, Allen C, Chawla D, Brandt C, Taylor A (2020) Benchmark and best practices for biomedical knowledge graph embeddings. In: Proceedings of the 19th SIGBioMed Workshop on Biomedical Language Processing, pp. 167\u2013176. https:\/\/doi.org\/10.18653\/v1\/2020.bionlp-1.18","DOI":"10.18653\/v1\/2020.bionlp-1.18"},{"key":"8303_CR32","first-page":"279","volume":"121","author":"K Donnelly","year":"2006","unstructured":"Donnelly K et al (2006) SNOMED-CT: the advanced terminology and coding system for eHealth. Stud Health Technol Inform 121:279","journal-title":"Stud Health Technol Inform"},{"key":"8303_CR33","doi-asserted-by":"publisher","unstructured":"Sun M, Xing J, Wang H, Chen B, Zhou J (2021) Mocl: data-driven molecular fingerprint via knowledge-aware contrastive learning from molecular graph. In: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, pp 3585\u20133594. https:\/\/doi.org\/10.1145\/3447548.3467186","DOI":"10.1145\/3447548.3467186"},{"key":"8303_CR34","doi-asserted-by":"publisher","first-page":"3968","DOI":"10.1609\/aaai.v36i4.20313","volume":"36","author":"Y Fang","year":"2022","unstructured":"Fang Y, Zhang Q, Yang H, Zhuang X, Deng S, Zhang W, Qin M, Chen Z, Fan X, Chen H (2022) Molecular contrastive learning with chemical element knowledge graph. In Proc AAAI Conf Artif Intell 36:3968\u20133976. https:\/\/doi.org\/10.1609\/aaai.v36i4.20313","journal-title":"In Proc AAAI Conf Artif Intell"},{"key":"8303_CR35","doi-asserted-by":"publisher","unstructured":"Fan Z, Yang Y, Xu M, Chen H (2023) Node-based knowledge graph contrastive learning for medical relationship prediction. https:\/\/doi.org\/10.48550\/arXiv.2310.10138","DOI":"10.48550\/arXiv.2310.10138"},{"key":"8303_CR36","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2025.112078","volume":"170","author":"M Wei","year":"2026","unstructured":"Wei M, Wang L, Su X, Zhao B, You Z (2026) Multi-hop graph structural modeling for cancer-related circRNA-miRNA interaction prediction. Pattern Recognit 170:112078. https:\/\/doi.org\/10.1016\/j.patcog.2025.112078","journal-title":"Pattern Recognit"},{"key":"8303_CR37","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2025.3561197","author":"M-M Wei","year":"2025","unstructured":"Wei M-M, Wang L, Zhao B-W, Su X-R, You Z-H, Huang D-S (2025) Integrating transformer and graph attention network for circRNA-miRNA interaction prediction. IEEE J Biomed Health Inform. https:\/\/doi.org\/10.1109\/JBHI.2025.3561197","journal-title":"IEEE J Biomed Health Inform"},{"key":"8303_CR38","doi-asserted-by":"publisher","unstructured":"Liu T, Wang S, Qiao S, Zhao Y, Zhang K, Tan X, Wu W, Wang S (2025) A rapid prediction model for miRNA-LncRNA interactions utilizing multi-view projection fusion and random parallel matrix factorization. Int J Biol Macromol. https:\/\/doi.org\/10.1016\/j.ijbiomac.2025.145404","DOI":"10.1016\/j.ijbiomac.2025.145404"},{"issue":"22","key":"8303_CR39","doi-asserted-by":"publisher","first-page":"8641","DOI":"10.1021\/acs.jcim.4c01589","volume":"64","author":"T Liu","year":"2024","unstructured":"Liu T, Wang S, Zhang Y, Li Y, Liu Y, Huang S (2024) Tiwmflp: two-tier interactive weighted matrix factorization and label propagation based on similarity matrix fusion for drug-disease association prediction. J Chem Inf Model 64(22):8641\u20138654. https:\/\/doi.org\/10.1021\/acs.jcim.4c01589","journal-title":"J Chem Inf Model"},{"issue":"4","key":"8303_CR40","doi-asserted-by":"publisher","first-page":"2158","DOI":"10.1021\/acs.jcim.4c02276","volume":"65","author":"T Liu","year":"2025","unstructured":"Liu T, Wang S, Pang S, Tan X (2025) Truncated arctangent rank minimization and double-strategy neighborhood constraint graph inference for drug-disease association prediction. J Chem Inf Model 65(4):2158\u20132172. https:\/\/doi.org\/10.1021\/acs.jcim.4c02276","journal-title":"J Chem Inf Model"},{"issue":"4","key":"8303_CR41","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1093\/bib\/bbad234","volume":"24","author":"S Wang","year":"2023","unstructured":"Wang S, Liu T, Ren C, Wu W, Zhao Z, Pang S, Zhang Y (2023) Predicting potential small molecule-miRNA associations utilizing truncated Schatten p-norm. Brief Bioinform 24(4):234. https:\/\/doi.org\/10.1093\/bib\/bbad234","journal-title":"Brief Bioinform"},{"issue":"2","key":"8303_CR42","doi-asserted-by":"publisher","first-page":"847","DOI":"10.1007\/s10115-019-01328-3","volume":"61","author":"N Mazaheri Soudani","year":"2019","unstructured":"Mazaheri Soudani N, Fatemi A, Nematbakhsh M (2019) PPR-partitioning: a distributed graph partitioning algorithm based on the personalized Pagerank vectors in vertex-centric systems. Knowl Inf Syst 61(2):847\u2013871. https:\/\/doi.org\/10.1007\/s10115-019-01328-3","journal-title":"Knowl Inf Syst"},{"issue":"12","key":"8303_CR43","doi-asserted-by":"publisher","first-page":"5509","DOI":"10.1007\/s13042-024-02256-7","volume":"15","author":"A Karami","year":"2024","unstructured":"Karami A, Ramezani R, Baraani Dastjerdi A (2024) GFD-SSL: generative federated knowledge distillation-based semi-supervised learning. Int J Mach Learn Cybern 15(12):5509\u20135529. https:\/\/doi.org\/10.1007\/s13042-024-02256-7","journal-title":"Int J Mach Learn Cybern"},{"issue":"suppl\u20131","key":"8303_CR44","doi-asserted-by":"publisher","first-page":"267","DOI":"10.1093\/nar\/gkh061","volume":"32","author":"O Bodenreider","year":"2004","unstructured":"Bodenreider O (2004) The unified medical language system (UMLS): integrating biomedical terminology. Nucleic Acids Res 32(suppl\u20131):267\u2013270. https:\/\/doi.org\/10.1093\/nar\/gkh061","journal-title":"Nucleic Acids Res"},{"issue":"D1","key":"8303_CR45","doi-asserted-by":"publisher","first-page":"1074","DOI":"10.1093\/nar\/gkx1037","volume":"46","author":"DS Wishart","year":"2018","unstructured":"Wishart DS, Feunang YD, Guo AC, Lo EJ, Marcu A, Grant JR, Sajed T, Johnson D, Li C, Sayeeda Z et al (2018) Drugbank 5.0: a major update to the drugbank database for 2018. Nucleic Acids Res 46(D1):1074\u20131082. https:\/\/doi.org\/10.1093\/nar\/gkx1037","journal-title":"Nucleic Acids Res"},{"key":"8303_CR46","doi-asserted-by":"publisher","unstructured":"Veli\u010dkovi\u0107 P, Cucurull G, Casanova A, Romero A, Lio P, Bengio Y (2017) Graph attention networks. https:\/\/doi.org\/10.48550\/arXiv.1710.1090","DOI":"10.48550\/arXiv.1710.1090"},{"key":"8303_CR47","unstructured":"NVIDIA Corporation: NCCL: NVIDIA Collective Communications Library (2022) https:\/\/developer.nvidia.com\/nccl"}],"container-title":["The Journal of Supercomputing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-026-08303-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11227-026-08303-0","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-026-08303-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,11]],"date-time":"2026-02-11T06:38:57Z","timestamp":1770791937000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11227-026-08303-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2,11]]},"references-count":47,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2026,2]]}},"alternative-id":["8303"],"URL":"https:\/\/doi.org\/10.1007\/s11227-026-08303-0","relation":{},"ISSN":["1573-0484"],"issn-type":[{"value":"1573-0484","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,2,11]]},"assertion":[{"value":"18 November 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 January 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 February 2026","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 Conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"151"}}