{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,6]],"date-time":"2025-11-06T12:40:11Z","timestamp":1762432811438,"version":"build-2065373602"},"reference-count":43,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,11,6]],"date-time":"2025-11-06T00:00:00Z","timestamp":1762387200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,11,6]],"date-time":"2025-11-06T00:00:00Z","timestamp":1762387200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"National Institute of Health, United States","award":["NCI 5P30 CA013696"],"award-info":[{"award-number":["NCI 5P30 CA013696"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Bioinformatics"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Spatial transcriptomics have emerged as a powerful tool in biomedical research because of its ability to capture both the spatial contexts and abundance of the complete RNA transcript profile in organs of interest. However, limitations of the technology such as the relatively low resolution and comparatively insufficient sequencing depth make it difficult to reliably extract real biological signals from these data. To alleviate this challenge, we propose a novel transfer learning framework, referred to as TransST, to adaptively leverage the cell-labeled information from external sources in inferring cell-level heterogeneity of a target spatial transcriptomics data.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Applications in several real studies as well as a number of simulation settings show that our approach significantly improves existing techniques. For example, in the breast cancer study, TransST successfully identifies five biologically meaningful cell clusters, including the two subgroups of cancer in situ and invasive cancer; in addition, only TransST is able to separate the adipose tissues from the connective issues among all the studied methods.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>In summary, the proposed method TransST is both effective and robust in identifying cell subclusters and detecting corresponding driving biomarkers in spatial transcriptomics data.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s12859-025-06099-z","type":"journal-article","created":{"date-parts":[[2025,11,6]],"date-time":"2025-11-06T12:36:07Z","timestamp":1762432567000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["TransST: transfer learning embedded spatial factor modeling of spatial transcriptomics data"],"prefix":"10.1186","volume":"26","author":[{"given":"Shuo Shuo","family":"Liu","sequence":"first","affiliation":[]},{"given":"Shikun","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Yuxuan","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Anil K.","family":"Rustgi","sequence":"additional","affiliation":[]},{"given":"Ming","family":"Yuan","sequence":"additional","affiliation":[]},{"given":"Jianhua","family":"Hu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,11,6]]},"reference":[{"issue":"1","key":"6099_CR1","doi-asserted-by":"publisher","first-page":"466","DOI":"10.1093\/bib\/bbab466","volume":"23","author":"Y Yang","year":"2022","unstructured":"Yang Y, Shi X, Liu W, Zhou Q, Chan Lau M, Chun Tatt Lim J, Sun L, Ng CCY, Yeong J, Liu J. SC-MEB: spatial clustering with hidden markov random field using empirical Bayes. Br Bioinform. 2022;23(1):466.","journal-title":"Br Bioinform"},{"issue":"5","key":"6099_CR2","doi-asserted-by":"publisher","first-page":"703","DOI":"10.1038\/s41587-021-01161-6","volume":"40","author":"Y Lin","year":"2022","unstructured":"Lin Y, Wu T-Y, Wan S, Yang JY, Wong WH, Wang YR. scjoint integrates atlas-scale single-cell RNA-seq and ATAC-seq data with transfer learning. Nat Biotechnol. 2022;40(5):703\u201310.","journal-title":"Nat Biotechnol"},{"issue":"1","key":"6099_CR3","doi-asserted-by":"publisher","first-page":"74","DOI":"10.1038\/s41587-021-01006-2","volume":"40","author":"T Lohoff","year":"2022","unstructured":"Lohoff T, Ghazanfar S, Missarova A, Koulena N, Pierson N, Griffiths J, Bardot E, Eng C-H, Tyser R, Argelaguet R, et al. Integration of spatial and single-cell transcriptomic data elucidates mouse organogenesis. Nat Biotechnol. 2022;40(1):74\u201385.","journal-title":"Nat Biotechnol"},{"issue":"2","key":"6099_CR4","doi-asserted-by":"publisher","first-page":"497","DOI":"10.1016\/j.cell.2020.05.039","volume":"182","author":"AL Ji","year":"2020","unstructured":"Ji AL, Rubin AJ, Thrane K, Jiang S, Reynolds DL, Meyers RM, Guo MG, George BM, Mollbrink A, Bergenstr\u00e5hle J, et al. Multimodal analysis of composition and spatial architecture in human squamous cell carcinoma. Cell. 2020;182(2):497\u2013514.","journal-title":"Cell"},{"issue":"11","key":"6099_CR5","doi-asserted-by":"publisher","first-page":"1342","DOI":"10.1038\/s41592-021-01255-8","volume":"18","author":"J Hu","year":"2021","unstructured":"Hu J, Li X, Coleman K, Schroeder A, Ma N, Irwin DJ, Lee EB, Shinohara RT, Li M. SpaGCN: Integrating gene expression, spatial location and histology to identify spatial domains and spatially variable genes by graph convolutional network. Nat Methods. 2021;18(11):1342\u201351.","journal-title":"Nat Methods"},{"key":"6099_CR6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13059-021-02286-2","volume":"22","author":"R Dries","year":"2021","unstructured":"Dries R, Zhu Q, Dong R, Eng C-HL, Li H, Liu K, Fu Y, Zhao T, Sarkar A, Bao F, et al. Giotto: a toolbox for integrative analysis and visualization of spatial expression data. Genome Biol. 2021;22:1\u201331.","journal-title":"Genome Biol"},{"issue":"11","key":"6099_CR7","doi-asserted-by":"publisher","first-page":"1375","DOI":"10.1038\/s41587-021-00935-2","volume":"39","author":"E Zhao","year":"2021","unstructured":"Zhao E, Stone MR, Ren X, Guenthoer J, Smythe KS, Pulliam T, Williams SR, Uytingco CR, Taylor SE, Nghiem P, et al. Spatial transcriptomics at subspot resolution with bayesspace. Nat Biotechnol. 2021;39(11):1375\u201384.","journal-title":"Nat Biotechnol"},{"key":"6099_CR8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.18637\/jss.v091.i10","volume":"91","author":"A Markos","year":"2019","unstructured":"Markos A, D\u2019Enza AI, Velden M. Beyond tandem analysis: joint dimension reduction and clustering in R. J Stat Softw. 2019;91:1\u201324.","journal-title":"J Stat Softw"},{"key":"6099_CR9","doi-asserted-by":"crossref","unstructured":"Aggarwal CC, Yu PS (2000) Finding generalized projected clusters in high dimensional spaces. In: Proceedings of the 2000 ACM SIGMOD international conference on management of data, pp 70\u201381","DOI":"10.1145\/342009.335383"},{"issue":"12","key":"6099_CR10","doi-asserted-by":"publisher","first-page":"72","DOI":"10.1093\/nar\/gkac219","volume":"50","author":"W Liu","year":"2022","unstructured":"Liu W, Liao X, Yang Y, Lin H, Yeong J, Zhou X, Shi X, Liu J. Joint dimension reduction and clustering analysis of single-cell RNA-seq and spatial transcriptomics data. Nucleic Acids Res. 2022;50(12):72\u201372.","journal-title":"Nucleic Acids Res"},{"issue":"1","key":"6099_CR11","doi-asserted-by":"publisher","first-page":"20353","DOI":"10.1038\/s41598-019-56911-z","volume":"9","author":"B Mieth","year":"2019","unstructured":"Mieth B, Hockley JR, G\u00f6rnitz N, Vidovic MM-C, M\u00fcller K-R, Gutteridge A, Ziemek D. Using transfer learning from prior reference knowledge to improve the clustering of single-cell RNA-seq data. Sci Rep. 2019;9(1):20353.","journal-title":"Sci Rep"},{"issue":"10","key":"6099_CR12","doi-asserted-by":"publisher","first-page":"2024383118","DOI":"10.1073\/pnas.2024383118","volume":"118","author":"M Peng","year":"2021","unstructured":"Peng M, Li Y, Wamsley B, Wei Y, Roeder K. Integration and transfer learning of single-cell transcriptomes via cFIT. Proc Natl Acad Sci. 2021;118(10):2024383118.","journal-title":"Proc Natl Acad Sci"},{"issue":"10","key":"6099_CR13","doi-asserted-by":"publisher","first-page":"607","DOI":"10.1038\/s42256-020-00233-7","volume":"2","author":"J Hu","year":"2020","unstructured":"Hu J, Li X, Hu G, Lyu Y, Susztak K, Li M. Iterative transfer learning with neural network for clustering and cell type classification in single-cell RNA-seq analysis. Nat Mach Intell. 2020;2(10):607\u201318.","journal-title":"Nat Mach Intell"},{"key":"6099_CR14","doi-asserted-by":"crossref","unstructured":"Hao Y, Stuart T, Kowalski MH, Choudhary S, Hoffman P, Hartman A, Srivastava A, Molla G, Madad S, Fernandez-Granda C, et al. (2023) Dictionary learning for integrative, multimodal and scalable single-cell analysis. Nat Biotechnol, 1\u201312.","DOI":"10.1101\/2022.02.24.481684"},{"issue":"10","key":"6099_CR15","doi-asserted-by":"publisher","first-page":"10008","DOI":"10.1088\/1742-5468\/2008\/10\/P10008","volume":"2008","author":"VD Blondel","year":"2008","unstructured":"Blondel VD, Guillaume J-L, Lambiotte R, Lefebvre E. Fast unfolding of communities in large networks. J Stat Mech Theory Exp. 2008;2008(10):10008.","journal-title":"J Stat Mech Theory Exp"},{"issue":"1","key":"6099_CR16","doi-asserted-by":"publisher","first-page":"5233","DOI":"10.1038\/s41598-019-41695-z","volume":"9","author":"VA Traag","year":"2019","unstructured":"Traag VA, Waltman L, Van Eck NJ. From Louvain to Leiden: guaranteeing well-connected communities. Sci Rep. 2019;9(1):5233.","journal-title":"Sci Rep"},{"issue":"1","key":"6099_CR17","doi-asserted-by":"publisher","first-page":"1155","DOI":"10.1038\/s41467-023-36796-3","volume":"14","author":"Y Long","year":"2023","unstructured":"Long Y, Ang KS, Li M, Chong KLK, Sethi R, Zhong C, Xu H, Ong Z, Sachaphibulkij K, Chen A, et al. Spatially informed clustering, integration, and deconvolution of spatial transcriptomics with GraphST. Nat Commun. 2023;14(1):1155.","journal-title":"Nat Commun"},{"issue":"1","key":"6099_CR18","doi-asserted-by":"publisher","first-page":"6012","DOI":"10.1038\/s41467-021-26271-2","volume":"12","author":"A Andersson","year":"2021","unstructured":"Andersson A, Larsson L, Stenbeck L, Salm\u00e9n F, Ehinger A, Wu SZ, Al-Eryani G, Roden D, Swarbrick A, Borg \u00c5, et al. Spatial deconvolution of HER2-positive breast cancer delineates tumor-associated cell type interactions. Nat Commun. 2021;12(1):6012.","journal-title":"Nat Commun"},{"issue":"3","key":"6099_CR19","first-page":"1249","volume":"29","author":"J Wu","year":"2020","unstructured":"Wu J, Luo M, Chen Z, Huang X. Comprehensive analysis of CD2 in the immune microenvironment of breast cancer. Revista Argentina de Cl\u00ednica Psicol\u00f3gica. 2020;29(3):1249\u201356.","journal-title":"Revista Argentina de Cl\u00ednica Psicol\u00f3gica"},{"key":"6099_CR20","doi-asserted-by":"publisher","DOI":"10.3389\/fimmu.2021.664845","volume":"12","author":"Y Chen","year":"2021","unstructured":"Chen Y, Meng Z, Zhang L, Liu F. CD2 is a novel immune-related prognostic biomarker of invasive breast carcinoma that modulates the tumor microenvironment. Front Immunol. 2021;12: 664845.","journal-title":"Front Immunol"},{"key":"6099_CR21","doi-asserted-by":"publisher","first-page":"667","DOI":"10.1007\/s10434-001-0667-3","volume":"8","author":"F Vizoso","year":"2001","unstructured":"Vizoso F, Plaza E, V\u00e1zquez J, Serra C, Lamelas ML, Gonz\u00e1lez LO, Merino AM, M\u00e9ndez J. Lysozyme expression by breast carcinomas, correlation with clinicopathologic parameters, and prognostic significance. Ann Surg Oncol. 2001;8:667\u201374.","journal-title":"Ann Surg Oncol"},{"key":"6099_CR22","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/bcr537","volume":"4","author":"C Serra","year":"2002","unstructured":"Serra C, Vizoso F, Alonso L, Rodr\u00edguez JC, Gonz\u00e1lez LO, Fern\u00e1ndez M, Lamelas ML, S\u00e1nchez LM, Garc\u00eda-Mu\u00f1iz JL, Baltasar A, et al. Expression and prognostic significance of lysozyme in male breast cancer. Breast Cancer Res. 2002;4:1\u20138.","journal-title":"Breast Cancer Res"},{"issue":"1","key":"6099_CR23","doi-asserted-by":"publisher","first-page":"98","DOI":"10.1038\/s41467-022-35238-w","volume":"14","author":"S Tietscher","year":"2023","unstructured":"Tietscher S, Wagner J, Anzeneder T, Langwieder C, Rees M, Sobottka B, Souza N, Bodenmiller B. A comprehensive single-cell map of t cell exhaustion-associated immune environments in human breast cancer. Nat Commun. 2023;14(1):98.","journal-title":"Nat Commun"},{"issue":"10","key":"6099_CR24","doi-asserted-by":"publisher","first-page":"5361","DOI":"10.21873\/anticanres.13729","volume":"39","author":"HY Woo","year":"2019","unstructured":"Woo HY, Do S-I, Kim SH, Song SY, Kim H-S. Promoter methylation down-regulates B-cell translocation gene 1 expression in breast carcinoma. Anticancer Res. 2019;39(10):5361\u20137.","journal-title":"Anticancer Res"},{"issue":"8","key":"6099_CR25","doi-asserted-by":"publisher","first-page":"0221413","DOI":"10.1371\/journal.pone.0221413","volume":"14","author":"A Amirfallah","year":"2019","unstructured":"Amirfallah A, Arason A, Einarsson H, Gudmundsdottir ET, Freysteinsdottir ES, Olafsdottir KA, Johannsson OT, Agnarsson BA, Barkardottir RB, Reynisdottir I. High expression of the vacuole membrane protein 1 (vmp1) is a potential marker of poor prognosis in HER2 positive breast cancer. PLoS ONE. 2019;14(8):0221413.","journal-title":"PLoS ONE"},{"issue":"1","key":"6099_CR26","doi-asserted-by":"publisher","first-page":"117","DOI":"10.1038\/labinvest.2017.106","volume":"98","author":"M Raap","year":"2018","unstructured":"Raap M, Gronewold M, Christgen H, Glage S, Bentires-Alj M, Koren S, Derksen PW, Boelens M, Jonkers J, Lehmann U, et al. Lobular carcinoma in situ and invasive lobular breast cancer are characterized by enhanced expression of transcription factor ap-2$$\\beta$$. Lab Invest. 2018;98(1):117\u201329.","journal-title":"Lab Invest"},{"issue":"13","key":"6099_CR27","first-page":"2896","volume":"55","author":"JA Byrne","year":"1995","unstructured":"Byrne JA, Tomasetto C, Garnier J-M, Rouyer N, Mattei M-G, Bellocq J-P, Rio M-C, Basset P. A screening method to identify genes commonly overexpressed in carcinomas and the identification of a novel complementary dna sequence. Can Res. 1995;55(13):2896\u2013903.","journal-title":"Can Res"},{"issue":"1","key":"6099_CR28","doi-asserted-by":"publisher","first-page":"48","DOI":"10.1002\/1098-2264(2000)9999:9999<::AID-GCC1005>3.0.CO;2-O","volume":"29","author":"RL Balleine","year":"2000","unstructured":"Balleine RL, Fejzo MS, Sathasivam P, Basset P, Clarke CL, Byrne JA. The hD52 (TPD52) gene is a candidate target gene for events resulting in increased 8q21 copy number in human breast carcinoma. Genes Chromosom Cancer. 2000;29(1):48\u201357.","journal-title":"Genes Chromosom Cancer"},{"key":"6099_CR29","doi-asserted-by":"publisher","DOI":"10.1016\/j.gene.2019.144088","volume":"720","author":"W Xie","year":"2019","unstructured":"Xie W, Zhang H, Qin S, Zhang J, Fan X, Yin Y, Liang R, Long H, Yi W, Fu D, et al. The expression and clinical significance of secretory leukocyte proteinase inhibitor (SLPI) in mammary carcinoma using bioinformatics analysis. Gene. 2019;720: 144088.","journal-title":"Gene"},{"key":"6099_CR30","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s43042-020-00124-x","volume":"22","author":"AM Nomair","year":"2021","unstructured":"Nomair AM, Ahmed SS, Mohammed AF, El Mansy H, Nomeir HM. SCGB3A1 gene DNA methylation status is associated with breast cancer in Egyptian female patients. Egypt J Med Human Gen. 2021;22:1\u201312.","journal-title":"Egypt J Med Human Gen"},{"issue":"4","key":"6099_CR31","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3892\/etm.2021.9736","volume":"21","author":"P Zhong","year":"2021","unstructured":"Zhong P, Shu R, Wu H, Liu Z, Shen X, Hu Y. Low KRT15 expression is associated with poor prognosis in patients with breast invasive carcinoma. Exp Ther Med. 2021;21(4):1\u20131.","journal-title":"Exp Ther Med"},{"issue":"12","key":"6099_CR32","doi-asserted-by":"publisher","first-page":"2492","DOI":"10.1158\/1541-7786.MCR-19-0264","volume":"17","author":"I Sirois","year":"2019","unstructured":"Sirois I, Aguilar-Mahecha A, Lafleur J, Fowler E, Vu V, Scriver M, Buchanan M, Chabot C, Ramanathan A, Balachandran B, et al. A unique morphological phenotype in chemoresistant triple-negative breast cancer reveals metabolic reprogramming and plin4 expression as a molecular vulnerability. Mol Cancer Res. 2019;17(12):2492\u2013507.","journal-title":"Mol Cancer Res"},{"issue":"6","key":"6099_CR33","doi-asserted-by":"publisher","first-page":"694","DOI":"10.1016\/j.devcel.2015.07.003","volume":"34","author":"S Gopalakrishnan","year":"2015","unstructured":"Gopalakrishnan S, Comai G, Sambasivan R, Francou A, Kelly RG, Tajbakhsh S. A cranial mesoderm origin for esophagus striated muscles. Dev Cell. 2015;34(6):694\u2013704.","journal-title":"Dev Cell"},{"issue":"5","key":"6099_CR34","doi-asserted-by":"publisher","first-page":"141","DOI":"10.1095\/biolreprod.112.098871","volume":"86","author":"SN Reardon","year":"2012","unstructured":"Reardon SN, King ML, MacLean JA, Mann JL, DeMayo FJ, Lydon JP, Hayashi K. Cdh1 is essential for endometrial differentiation, gland development, and adult function in the mouse uterus. Biol Reprod. 2012;86(5):141\u20131.","journal-title":"Biol Reprod"},{"issue":"1","key":"6099_CR35","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1093\/jb\/mvm122","volume":"142","author":"M Kobayashi","year":"2007","unstructured":"Kobayashi M, Yamamoto M. Regulation of GATA1 gene expression. J Biochem. 2007;142(1):1\u201310.","journal-title":"J Biochem"},{"issue":"10","key":"6099_CR36","doi-asserted-by":"publisher","first-page":"1867","DOI":"10.1242\/dev.125.10.1867","volume":"125","author":"A Isaac","year":"1998","unstructured":"Isaac A, Rodriguez-Esteban C, Ryan A, Altabef M, Tsukui T, Patel K, Tickle C, Izpis\u00faa-Belmonte J-C. Tbx genes and limb identity in chick embryo development. Development. 1998;125(10):1867\u201375.","journal-title":"Development"},{"issue":"15","key":"6099_CR37","doi-asserted-by":"publisher","first-page":"2935","DOI":"10.1242\/dev.124.15.2935","volume":"124","author":"FD Porter","year":"1997","unstructured":"Porter FD, Drago J, Xu Y, Cheema SS, Wassif C, Huang S-P, Lee E, Grinberg A, Massalas JS, Bodine D, et al. Lhx2, a lim homeobox gene, is required for eye, forebrain, and definitive erythrocyte development. Development. 1997;124(15):2935\u201344.","journal-title":"Development"},{"issue":"1","key":"6099_CR38","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1089\/ars.2009.2886","volume":"13","author":"Y Xu","year":"2010","unstructured":"Xu Y, Morse LR, Da Silva RAB, Odgren PR, Sasaki H, Stashenko P, Battaglino RA. PAMM: a redox regulatory protein that modulates osteoclast differentiation. Antioxid Redox Signal. 2010;13(1):27\u201337.","journal-title":"Antioxid Redox Signal"},{"issue":"1","key":"6099_CR39","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1111\/bjd.17316","volume":"180","author":"R O\u2019Shaughnessy","year":"2019","unstructured":"O\u2019Shaughnessy R. Targeting tryptophan transport and breakdown in basal cell carcinoma. Br J Dermatol. 2019;180(1):16\u20137.","journal-title":"Br J Dermatol"},{"key":"6099_CR40","doi-asserted-by":"crossref","unstructured":"Luan H, He Y, Jian L, Zhang T, Zhou L (2021) Identification of metastasis-associated gene and its correlation with immune infiltrates for skin cutaneous melanoma","DOI":"10.21203\/rs.3.rs-1059356\/v1"},{"issue":"1","key":"6099_CR41","doi-asserted-by":"publisher","first-page":"122","DOI":"10.1186\/s12935-023-02962-2","volume":"23","author":"S He","year":"2023","unstructured":"He S, Ding Y, Ji Z, Yuan B, Chen J, Ren W. HOPX is a tumor-suppressive biomarker that corresponds to t cell infiltration in skin cutaneous melanoma. Cancer Cell Int. 2023;23(1):122.","journal-title":"Cancer Cell Int"},{"issue":"2","key":"6099_CR42","doi-asserted-by":"publisher","first-page":"8187","DOI":"10.4238\/gmr.15028187","volume":"15","author":"Y Halifu","year":"2016","unstructured":"Halifu Y, Liang J, Zeng X, Ding Y, Zhang X, Jin T, Yakeya B, Abudu D, Zhou Y, Liu X, et al. Wnt1 and SFRP1 as potential prognostic factors and therapeutic targets in cutaneous squamous cell carcinoma. Genet Mol Res. 2016;15(2):8187.","journal-title":"Genet Mol Res"},{"key":"6099_CR43","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12885-021-08604-y","volume":"21","author":"H Chen","year":"2021","unstructured":"Chen H, Yang J, Wu W. Seven key hub genes identified by gene co-expression network in cutaneous squamous cell carcinoma. BMC Cancer. 2021;21:1\u201312.","journal-title":"BMC Cancer"}],"container-title":["BMC Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12859-025-06099-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12859-025-06099-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12859-025-06099-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,6]],"date-time":"2025-11-06T12:36:11Z","timestamp":1762432571000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcbioinformatics.biomedcentral.com\/articles\/10.1186\/s12859-025-06099-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,6]]},"references-count":43,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["6099"],"URL":"https:\/\/doi.org\/10.1186\/s12859-025-06099-z","relation":{},"ISSN":["1471-2105"],"issn-type":[{"value":"1471-2105","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11,6]]},"assertion":[{"value":"11 August 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 February 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 November 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":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare that they have no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"274"}}