{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T01:05:33Z","timestamp":1780448733771,"version":"3.54.1"},"reference-count":45,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T00:00:00Z","timestamp":1774483200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T00:00:00Z","timestamp":1780444800000},"content-version":"vor","delay-in-days":69,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Bioinformatics"],"DOI":"10.1186\/s12859-026-06378-3","type":"journal-article","created":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T12:02:09Z","timestamp":1774526529000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Triad-LMF: a hierarchical low-rank multimodal fusion framework for robust cancer subtype classification using multi-omics data"],"prefix":"10.1186","volume":"27","author":[{"given":"Xingyue","family":"Tan","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiran","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Renjie","family":"Tian","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qinyu","family":"Cai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Miaoyuan","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dongqiu","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lei","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,3,26]]},"reference":[{"issue":"5","key":"6378_CR1","doi-asserted-by":"publisher","first-page":"1476","DOI":"10.1093\/bioinformatics\/btz769","volume":"36","author":"X Chen","year":"2020","unstructured":"Chen X, Wang Y, Zhang Z, Zhang J, Zhang Y. Deep-learning approach to identifying cancer subtypes using high-dimensional omics data. Bioinformatics. 2020;36(5):1476\u201383. https:\/\/doi.org\/10.1093\/bioinformatics\/btz769.","journal-title":"Bioinformatics"},{"issue":"1","key":"6378_CR2","doi-asserted-by":"publisher","first-page":"168","DOI":"10.1186\/s12859-023-05272-6","volume":"24","author":"C ChoiJM, Park","year":"2023","unstructured":"Choi JM, Park C, Chae H. meth-SemiCancer: a cancer subtype classification framework via semi-supervised learning utilizing DNA methylation profiles. BMC Bioinformatics. 2023;24(1):168. https:\/\/doi.org\/10.1186\/s12859-023-05272-6","journal-title":"BMC Bioinformatics"},{"issue":"Suppl 5","key":"6378_CR3","doi-asserted-by":"publisher","first-page":"118","DOI":"10.1186\/s12859-018-2095-4","volume":"19","author":"Y Guo","year":"2018","unstructured":"Guo Y, Liu S, Li Z, et al. BCDForest: a boosting cascade deep forest model towards the classification of cancer subtypes based on gene expression data. BMC Bioinform. 2018;19(Suppl 5):118. https:\/\/doi.org\/10.1186\/s12859-018-2095-4.","journal-title":"BMC Bioinform"},{"key":"6378_CR4","doi-asserted-by":"publisher","DOI":"10.3389\/fgene.2022.806842","volume":"13","author":"X Li","year":"2022","unstructured":"Li X, Ma J, Leng L, Han M, Li M, He F, et al. MoGCN: a multi-omics integration method based on graph convolutional network for cancer subtype analysis. Front Genetics. 2022;13:806842. https:\/\/doi.org\/10.3389\/fgene.2022.806842.","journal-title":"Front Genetics"},{"key":"6378_CR5","doi-asserted-by":"publisher","unstructured":"Li S, Yang Y, Wang X, Li J, Yu J, Li X, Wong KC. Colorectal cancer subtype identification from differential gene expression levels using minimalist deep learning. BioData Min. 2022;15(1):12. . https:\/\/doi.org\/10.1186\/s13040-022-00295-w","DOI":"10.1186\/s13040-022-00295-w"},{"key":"6378_CR6","doi-asserted-by":"publisher","first-page":"3445","DOI":"10.1038\/s41467-021-23774-w","volume":"12","author":"T Wang","year":"2021","unstructured":"Wang T, Shao W, Huang Z, et al. MOGONET integrates multi-omics data using graph convolutional networks allowing patient classification and biomarker identification. Nat Commun. 2021;12:3445. https:\/\/doi.org\/10.1038\/s41467-021-23774-w","journal-title":"Nat Commun"},{"key":"6378_CR7","doi-asserted-by":"publisher","first-page":"1032768","DOI":"10.3389\/fgene.2022.1032768","volume":"13","author":"Q Sun","year":"2023","unstructured":"Sun Q, Cheng L, Meng A, Ge S, Chen J, Zhang L, et al. SADLN: self-attention based deep learning network of integrating multi-omics data for cancer subtype recognition. Front Genet. 2023;13:1032768. https:\/\/doi.org\/10.3389\/fgene.2022.1032768.","journal-title":"Front Genet"},{"key":"6378_CR8","doi-asserted-by":"publisher","DOI":"10.3389\/fgene.2022.884028","volume":"13","author":"C Yin","year":"2022","unstructured":"Yin C, Cao Y, Sun P, Zhang H, Li Z, Xu Y, et al. Molecular subtyping of cancer based on robust graph neural network and multi-omics data integration. Front Genet. 2022;13:884028. https:\/\/doi.org\/10.3389\/fgene.2022.884028.","journal-title":"Front Genet"},{"key":"6378_CR9","doi-asserted-by":"publisher","first-page":"979","DOI":"10.3389\/fgene.2020.00979","volume":"11","author":"Y Guo","year":"2020","unstructured":"Guo Y, et al. Kernel fusion method for detecting cancer subtypes via selecting relevant expression data. Front Genet. 2020;11:979. https:\/\/doi.org\/10.3389\/fgene.2020.00979.","journal-title":"Front Genet"},{"key":"6378_CR10","doi-asserted-by":"publisher","DOI":"10.3389\/fgene.2021.718915","author":"J Liu","year":"2021","unstructured":"Liu J, Ge S, Cheng Y, Wang X. Multi-view spectral clustering based on multi-smooth representation fusion for cancer subtype prediction. Front Genet. 2021. https:\/\/doi.org\/10.3389\/fgene.2021.718915.","journal-title":"Front Genet"},{"key":"6378_CR11","doi-asserted-by":"publisher","DOI":"10.3389\/fgene.2024.1466825","author":"J Liu","year":"2024","unstructured":"Liu J, Xue X, Wen P, Song Q, Yao J, Ge S. Multi-fusion strategy network-guided cancer subtypes discovering based on multi-omics data. Front Genet. 2024. https:\/\/doi.org\/10.3389\/fgene.2024.1466825.","journal-title":"Front Genet"},{"key":"6378_CR12","doi-asserted-by":"publisher","first-page":"1209","DOI":"10.1186\/s12864-024-11112-5","volume":"25","author":"J Wu","year":"2024","unstructured":"Wu J, Chen Z, Xiao S, et al. DeepMoIC: multi-omics data integration via deep graph convolutional networks for cancer subtype classification. BMC Genomics. 2024;25:1209. https:\/\/doi.org\/10.1186\/s12864-024-11112-5.","journal-title":"BMC Genomics"},{"key":"6378_CR13","doi-asserted-by":"publisher","DOI":"10.1016\/j.ailsci.2021.100019","volume":"1","author":"L Zhang","year":"2021","unstructured":"Zhang L, Shen W, Li P, Xu C, Liu D, He W, et al. Autoggn: a gene graph network automl tool for multi-omics research. Artif Intell Life Sci. 2021;1:100019. https:\/\/doi.org\/10.1016\/j.ailsci.2021.100019.","journal-title":"Artif Intell Life Sci"},{"key":"6378_CR14","doi-asserted-by":"publisher","first-page":"1363896","DOI":"10.3389\/fgene.2024.1363896","volume":"15","author":"Y RenY, Gao","year":"2024","unstructured":"Ren Y, Gao Y, Du W, Qiao W, Li W, Yang Q, Liang Y, Li G. Classifying breast cancer using multi-view graph neural network based on multi-omics data. Front Genet. 2024;15:1363896. https:\/\/doi.org\/10.3389\/fgene.2024.1363896","journal-title":"Front Genet"},{"key":"6378_CR15","doi-asserted-by":"publisher","DOI":"10.3389\/fgene.2022.866005","author":"P Sun","year":"2022","unstructured":"Sun P, Wu Y, Yin C, Jiang H, Xu Y, Sun H. Molecular subtyping of cancer based on distinguishing co-expression modules and machine learning. Front Genet. 2022. https:\/\/doi.org\/10.3389\/fgene.2022.866005.","journal-title":"Front Genet"},{"issue":"12","key":"6378_CR16","doi-asserted-by":"publisher","first-page":"1012710","DOI":"10.1371\/journal.pcbi.1012710","volume":"20","author":"Y Bu","year":"2024","unstructured":"Bu Y, Liang J, Li Z, Wang J, Wang J, Yu G. Cancer molecular subtyping using limited multi-omics data with missingness. PLoS Comput Biol. 2024;20(12):1012710. https:\/\/doi.org\/10.1371\/journal.pcbi.1012710.","journal-title":"PLoS Comput Biol"},{"key":"6378_CR17","doi-asserted-by":"publisher","first-page":"10912","DOI":"10.1109\/ACCESS.2023.3240515","volume":"11","author":"R Lupat","year":"2023","unstructured":"Lupat R, Perera R, Loi S, Li J. Moanna: multi-omics autoencoder-based neural network algorithm for predicting breast cancer subtypes. IEEE Access. 2023;11:10912\u201324. https:\/\/doi.org\/10.1109\/ACCESS.2023.3240515.","journal-title":"IEEE Access"},{"key":"6378_CR18","doi-asserted-by":"publisher","first-page":"1044026","DOI":"10.3389\/fonc.2022.1044026","volume":"12","author":"X JiangY, Sui","year":"2023","unstructured":"Jiang Y, Sui X, Ding Y, Xiao W, Zheng Y, Zhang Y. A semi-supervised learning approach with consistency regularization for tumor histopathological images analysis. Front Oncol. 2023;12:1044026. https:\/\/doi.org\/10.3389\/fonc.2022.1044026","journal-title":"Front Oncol"},{"key":"6378_CR19","doi-asserted-by":"publisher","unstructured":"Jose A, Srivastava A, Vinod PK. DeepGraphMut: a graph-based deep learning method for cancer prognosis using somatic mutation profile. bioRxiv. 2024. https:\/\/doi.org\/10.1101\/2024.12.03.626568. Preprint at bioRxiv. arxiv:2024.12036.","DOI":"10.1101\/2024.12.03.626568"},{"key":"6378_CR20","doi-asserted-by":"publisher","unstructured":"Sahay S, Okur E, Kumar SH, Nachman L. \u2018Low Rank Fusion Based Transformers for Multimodal Sequences\u2019. In Second Grand-Challenge and Workshop on Multimodal Language (Challenge-HML), edited by Amir Zadeh, Louis-Philippe Morency, Paul Pu Liang, and Soujanya Poria. Association for Computational Linguistics, 2020. https:\/\/doi.org\/10.18653\/v1\/2020.challengehml-1.4","DOI":"10.18653\/v1\/2020.challengehml-1.4"},{"issue":"8","key":"6378_CR21","doi-asserted-by":"publisher","first-page":"888","DOI":"10.3390\/genes11080888","volume":"11","author":"Y Lin","year":"2020","unstructured":"Lin Y, Zhang W, Cao H, Li G, Du W. Classifying breast cancer subtypes using deep neural networks based on multi-omics data. Genes. 2020;11(8):888.","journal-title":"Genes"},{"issue":"29","key":"6378_CR22","doi-asserted-by":"publisher","first-page":"861","DOI":"10.21105\/joss.00861","volume":"3","author":"L McInnes","year":"2018","unstructured":"McInnes L, Healy J, Saul N, Gro\u00dfberger L. Umap: uniform manifold approximation and projection. J Open Source Softw. 2018;3(29):861. https:\/\/doi.org\/10.21105\/joss.00861.","journal-title":"J Open Source Softw"},{"key":"6378_CR23","unstructured":"Lundberg SM, Lee S-I. A unified approach to interpreting model predictions. Adv Neural Inf Process Syst 2017;30."},{"issue":"11","key":"6378_CR24","doi-asserted-by":"publisher","first-page":"6141","DOI":"10.3390\/ijms25116141","volume":"25","author":"SK Marafie","year":"2024","unstructured":"Marafie SK, Al-Mulla F, Abubaker J. mTOR: its critical role in metabolic diseases, cancer, and the aging process. Int J Mol Sci. 2024;25(11):6141. https:\/\/doi.org\/10.3390\/ijms25116141.","journal-title":"Int J Mol Sci"},{"key":"6378_CR25","doi-asserted-by":"publisher","DOI":"10.1016\/j.cbi.2024.111055","volume":"396","author":"M Tufail","year":"2024","unstructured":"Tufail M, Wan W-D, Jiang C, Li N. Targeting pi3k\/akt\/mtor signaling to overcome drug resistance in cancer. Chemico-Biological Interact. 2024;396:111055. https:\/\/doi.org\/10.1016\/j.cbi.2024.111055.","journal-title":"Chemico-Biological Interact"},{"issue":"10","key":"6378_CR26","doi-asserted-by":"publisher","first-page":"870","DOI":"10.1007\/s12325-013-0060-1","volume":"30","author":"DA Yardley","year":"2013","unstructured":"Yardley DA, Noguchi S, Pritchard KI, Burris IHA, Baselga J, Gnant M, et al. Everolimus plus exemestane in postmenopausal patients with HR+ breast cancer: BOLERO-2 final progression-free survival analysis. Adv Ther. 2013;30(10):870\u201384. https:\/\/doi.org\/10.1007\/s12325-013-0060-1.","journal-title":"Adv Ther"},{"issue":"6797","key":"6378_CR27","doi-asserted-by":"publisher","first-page":"747","DOI":"10.1038\/35021093","volume":"406","author":"CM Perou","year":"2000","unstructured":"Perou CM, et al. Molecular portraits of human breast tumours. Nature. 2000;406(6797):747\u201352. https:\/\/doi.org\/10.1038\/35021093.","journal-title":"Nature"},{"key":"6378_CR28","doi-asserted-by":"publisher","DOI":"10.1146\/annurev-med-070909-182917","author":"CK Osborne","year":"2011","unstructured":"Osborne CK, Schiff R. Mechanisms of endocrine resistance in breast cancer. Annu Rev Med. 2011. https:\/\/doi.org\/10.1146\/annurev-med-070909-182917.","journal-title":"Annu Rev Med"},{"issue":"3","key":"6378_CR29","doi-asserted-by":"publisher","first-page":"359","DOI":"10.1586\/eem.11.25","volume":"6","author":"AR Daniel","year":"2011","unstructured":"Daniel AR, Hagan CR, Lange CA. Progesterone receptor action: defining a role in breast cancer. Expert Rev Endocrinol Metab. 2011;6(3):359\u201369. https:\/\/doi.org\/10.1586\/eem.11.25.","journal-title":"Expert Rev Endocrinol Metab"},{"issue":"12","key":"6378_CR30","doi-asserted-by":"publisher","first-page":"1501","DOI":"10.1101\/gad.13.12.1501","volume":"13","author":"CJ Sherr","year":"1999","unstructured":"Sherr CJ, Roberts JM. CDK inhibitors: positive and negative regulators of G1-phase progression. Genes Dev. 1999;13(12):1501\u201312. https:\/\/doi.org\/10.1101\/gad.13.12.1501.","journal-title":"Genes Dev"},{"issue":"10","key":"6378_CR31","doi-asserted-by":"publisher","first-page":"1136","DOI":"10.1038\/nm762","volume":"8","author":"G Viglietto","year":"2002","unstructured":"Viglietto G, et al. Cytoplasmic relocalization and inhibition of the cyclin-dependent kinase inhibitor p27(kip1) by pkb\/akt-mediated phosphorylation in breast cancer. Nat Med. 2002;8(10):1136\u201344. https:\/\/doi.org\/10.1038\/nm762.","journal-title":"Nat Med"},{"issue":"5","key":"6378_CR32","doi-asserted-by":"publisher","first-page":"668","DOI":"10.1038\/sj.bjc.6605736","volume":"103","author":"S-J Dawson","year":"2010","unstructured":"Dawson S-J, Makretsov N, Blows FM, et al. BCL2 in breast cancer: a favourable prognostic marker across molecular subtypes and independent of adjuvant therapy received. Br J Cancer. 2010;103(5):668\u201375. https:\/\/doi.org\/10.1038\/sj.bjc.6605736.","journal-title":"Br J Cancer"},{"issue":"5","key":"6378_CR33","doi-asserted-by":"publisher","first-page":"341","DOI":"10.1038\/nrc1609","volume":"5","author":"NE Hynes","year":"2005","unstructured":"Hynes NE, Lane HA. Erbb receptors and cancer: the complexity of targeted inhibitors. Nat Rev Cancer. 2005;5(5):341\u201354. https:\/\/doi.org\/10.1038\/nrc1609.","journal-title":"Nat Rev Cancer"},{"issue":"22","key":"6378_CR34","doi-asserted-by":"publisher","first-page":"3291","DOI":"10.1038\/sj.onc.1210422","volume":"26","author":"PJ Roberts","year":"2007","unstructured":"Roberts PJ, Der CJ. Targeting the raf-mek-erk mitogen-activated protein kinase cascade for the treatment of cancer. Oncogene. 2007;26(22):3291\u2013310. https:\/\/doi.org\/10.1038\/sj.onc.1210422.","journal-title":"Oncogene"},{"key":"6378_CR35","doi-asserted-by":"publisher","DOI":"10.1038\/nrc.2015.21","author":"CJ Lord","year":"2016","unstructured":"Lord CJ, Ashworth A. Brcaness revisited. Nat Rev Cancer. 2016. https:\/\/doi.org\/10.1038\/nrc.2015.21.","journal-title":"Nat Rev Cancer"},{"issue":"1","key":"6378_CR36","doi-asserted-by":"publisher","first-page":"7","DOI":"10.1038\/nrc3860","volume":"15","author":"LM Thorpe","year":"2015","unstructured":"Thorpe LM, et al. Pi3k in cancer: divergent roles of isoforms, modes of activation and therapeutic targeting. Nat Rev Cancer. 2015;15(1):7\u201324. https:\/\/doi.org\/10.1038\/nrc3860.","journal-title":"Nat Rev Cancer"},{"key":"6378_CR37","unstructured":"Ellis MJ et al. Whole-genome analysis informs breast cancer response to aromatase inhibition. Nature. 2012."},{"issue":"4","key":"6378_CR38","doi-asserted-by":"publisher","first-page":"361","DOI":"10.1158\/2326-6066.CIR-13-0127","volume":"2","author":"EA Mittendorf","year":"2014","unstructured":"Mittendorf EA, Philips AV, Meric-Bernstam F, Qiao N, Wu Y, Harrington S, et al. PD-L1 expression in triple-negative breast cancer. Cancer Immunol Res. 2014;2(4):361\u201370. https:\/\/doi.org\/10.1158\/2326-6066.CIR-13-0127.","journal-title":"Cancer Immunol Res"},{"issue":"1","key":"6378_CR39","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1038\/nrm3722","volume":"15","author":"PE Czabotar","year":"2014","unstructured":"Czabotar PE, et al. Control of apoptosis by the bcl-2 protein family: Implications for physiology and therapy. Nat Rev Mol Cell Biol. 2014;15(1):49\u201363. https:\/\/doi.org\/10.1038\/nrm3722.","journal-title":"Nat Rev Mol Cell Biol"},{"issue":"1","key":"6378_CR40","doi-asserted-by":"publisher","first-page":"68","DOI":"10.1038\/nrc3181","volume":"12","author":"R Roy","year":"2011","unstructured":"Roy R, Chun J, Powell SN. BRCA1 and BRCA2: different roles in a common pathway of genome protection. Nat Rev Cancer. 2011;12(1):68\u201378. https:\/\/doi.org\/10.1038\/nrc3181.","journal-title":"Nat Rev Cancer"},{"issue":"12","key":"6378_CR41","doi-asserted-by":"publisher","first-page":"3624","DOI":"10.1007\/s43032-024-01666-w","volume":"31","author":"P Pourmasoumi","year":"2024","unstructured":"Pourmasoumi P, Moradi A, Bayat M. BRCA1\/2 mutations and breast\/ovarian cancer risk: a new insights review. Reprod Sci. 2024;31(12):3624\u201334. https:\/\/doi.org\/10.1007\/s43032-024-01666-w.","journal-title":"Reprod Sci"},{"issue":"4","key":"6378_CR42","doi-asserted-by":"publisher","first-page":"605","DOI":"10.1016\/j.cell.2017.07.029","volume":"170","author":"DA Fruman","year":"2017","unstructured":"Fruman DA, Rommel C. The pi3k pathway in human disease. Cell. 2017;170(4):605\u201335. https:\/\/doi.org\/10.1016\/j.cell.2017.07.029.","journal-title":"Cell"},{"key":"6378_CR43","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2023.127181","volume":"572","author":"Y Wang","year":"2024","unstructured":"Wang Y, He J, Wang D, Wang Q, Wan B, Luo X. Multimodal transformer with adaptive modality weighting for multimodal sentiment analysis. Neurocomputing. 2024;572:127181. https:\/\/doi.org\/10.1016\/j.neucom.2023.127181.","journal-title":"Neurocomputing"},{"key":"6378_CR44","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2025.107821","volume":"191","author":"M Chen","year":"2025","unstructured":"Chen M, Yao J, Xing L, Wang Y, Zhang Y, Wang Y. Redundancy-adaptive multimodal learning for imperfect data. Neural Netw. 2025;191:107821. https:\/\/doi.org\/10.1016\/j.neunet.2025.107821.","journal-title":"Neural Netw"},{"issue":"9","key":"6378_CR45","doi-asserted-by":"publisher","DOI":"10.1016\/j.jmb.2025.169021","volume":"437","author":"X Chen","year":"2025","unstructured":"Chen X, Lin S, Chen X, Li W, Li Y. Timestamp calibration for time-series single cell rna-seq expression data. J Mol Biol. 2025;437(9):169021.","journal-title":"J Mol Biol"}],"container-title":["BMC Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12859-026-06378-3","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12859-026-06378-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12859-026-06378-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T00:15:52Z","timestamp":1780445752000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1186\/s12859-026-06378-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,26]]},"references-count":45,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["6378"],"URL":"https:\/\/doi.org\/10.1186\/s12859-026-06378-3","relation":{},"ISSN":["1471-2105"],"issn-type":[{"value":"1471-2105","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3,26]]},"assertion":[{"value":"26 August 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 January 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 March 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":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare no Conflict of interest.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"118"}}