{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,9]],"date-time":"2026-09-09T15:20:48Z","timestamp":1788967248759,"version":"build-2803163510"},"reference-count":30,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,8,11]],"date-time":"2026-08-11T00:00:00Z","timestamp":1786406400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2026,9,9]],"date-time":"2026-09-09T00:00:00Z","timestamp":1788912000000},"content-version":"vor","delay-in-days":29,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BioData Mining"],"DOI":"10.1186\/s13040-026-00593-7","type":"journal-article","created":{"date-parts":[[2026,8,11]],"date-time":"2026-08-11T06:58:24Z","timestamp":1786431504000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Integrative evidence-knowledge marker selection enhances LLM-based cell type annotation in single-cell RNA-seq analysis"],"prefix":"10.1186","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-3971-9981","authenticated-orcid":false,"given":"Jiyeong","family":"Shin","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0911-0567","authenticated-orcid":false,"given":"Soyoung","family":"Jeong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4467-0949","authenticated-orcid":false,"given":"Hyun Je","family":"Kim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4170-1543","authenticated-orcid":false,"given":"Minsik","family":"Oh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,8,11]]},"reference":[{"issue":"10","key":"593_CR1","doi-asserted-by":"publisher","first-page":"1516","DOI":"10.3390\/biom13101516","volume":"13","author":"J Elsborg","year":"2023","unstructured":"Elsborg J, Salvatore M. Using LLMs and explainable ML to analyze biomarkers at single-cell level for improved understanding of diseases. Biomolecules. 2023;13(10):1516. https:\/\/doi.org\/10.3390\/biom13101516.","journal-title":"Biomolecules"},{"key":"593_CR2","doi-asserted-by":"publisher","unstructured":"Luecken MD, Theis FJ. Current best practices in single-cell RNA-seq analysis: a tutorial. Mol Syst Biol. 2019;15(6):e8746. https:\/\/doi.org\/10.15252\/msb.20188746.","DOI":"10.15252\/msb.20188746"},{"issue":"3","key":"593_CR3","doi-asserted-by":"publisher","first-page":"133","DOI":"10.1038\/nrg3833","volume":"16","author":"O Stegle","year":"2015","unstructured":"Stegle O, Teichmann SA, Marioni JC. Computational and analytical challenges in single-cell transcriptomics. Nat Rev Genet. 2015;16(3):133\u201345. https:\/\/doi.org\/10.1038\/nrg3833.","journal-title":"Nat Rev Genet"},{"issue":"1","key":"593_CR4","doi-asserted-by":"publisher","first-page":"194","DOI":"10.1186\/s13059-019-1795-z","volume":"20","author":"T Abdelaal","year":"2019","unstructured":"Abdelaal T, Michielsen L, Cats D, Hoogduin D, Mei H, Reinders MJT, et al. A comparison of automatic cell identification methods for single-cell RNA sequencing data. Genome Biol. 2019;20(1):194. https:\/\/doi.org\/10.1186\/s13059-019-1795-z.","journal-title":"Genome Biol"},{"issue":"10","key":"593_CR5","doi-asserted-by":"publisher","first-page":"983","DOI":"10.1038\/s41592-019-0535-3","volume":"16","author":"HA Pliner","year":"2019","unstructured":"Pliner HA, Shendure J, Trapnell C. Supervised classification enables rapid annotation of cell atlases. Nat Methods. 2019;16(10):983\u201386. https:\/\/doi.org\/10.1038\/s41592-019-0535-3.","journal-title":"Nat Methods"},{"issue":"7","key":"593_CR6","doi-asserted-by":"publisher","first-page":"531","DOI":"10.3390\/genes10070531","volume":"10","author":"Z Zhang","year":"2019","unstructured":"Zhang Z, Luo D, Zhong X, Choi JH, Ma Y, Wang S, et al. SCINA: a semi-supervised subtyping algorithm of single cells and bulk samples. Genes. 2019;10(7):531. https:\/\/doi.org\/10.3390\/genes10070531.","journal-title":"Genes"},{"issue":"1","key":"593_CR7","doi-asserted-by":"publisher","first-page":"1246","DOI":"10.1038\/s41467-022-28803-w","volume":"13","author":"A Ianevski","year":"2022","unstructured":"Ianevski A, Giri AK, Aittokallio T. Fully-automated and ultra-fast cell-type identification using specific marker combinations from single-cell transcriptomic data. Nat Commun. 2022;13(1):1246. https:\/\/doi.org\/10.1038\/s41467-022-28803-w.","journal-title":"Nat Commun"},{"issue":"6","key":"593_CR8","doi-asserted-by":"publisher","first-page":"2749","DOI":"10.1038\/s41596-021-00534-0","volume":"16","author":"ZA Clarke","year":"2021","unstructured":"Clarke ZA, Andrews TS, Atif J, Pouyabahar D, Innes BT, MacParland SA, et al. Tutorial: guidelines for annotating single-cell transcriptomic maps using automated and manual methods. Nat Protoc. 2021;16(6):2749\u201364. https:\/\/doi.org\/10.1038\/s41596-021-00534-0.","journal-title":"Nat Protoc"},{"issue":"5","key":"593_CR9","doi-asserted-by":"publisher","first-page":"359","DOI":"10.1038\/nmeth.4644","volume":"15","author":"VY Kiselev","year":"2018","unstructured":"Kiselev VY, Yiu A, Hemberg M. Scmap: projection of single-cell RNA-seq data across data sets. Nat Methods. 2018;15(5):359\u201362. https:\/\/doi.org\/10.1038\/nmeth.4644.","journal-title":"Nat Methods"},{"issue":"2","key":"593_CR10","doi-asserted-by":"publisher","first-page":"163","DOI":"10.1038\/s41590-018-0276-y","volume":"20","author":"D Aran","year":"2019","unstructured":"Aran D, Looney AP, Liu L, Wu E, Fong V, Hsu A, et al. Reference-based analysis of lung single-cell sequencing reveals a transitional profibrotic macrophage. Nat Immunol. 2019;20(2):163\u201372. https:\/\/doi.org\/10.1038\/s41590-018-0276-y.","journal-title":"Nat Immunol"},{"key":"593_CR11","doi-asserted-by":"publisher","unstructured":"Dom\u00ednguez Conde C, Xu C, Jarvis LB, Rainbow DB, Wells SB, Gomes T, et al. Cross-tissue immune cell analysis reveals tissue-specific features in humans. Science. 2022;376(6594):eabl5197. https:\/\/doi.org\/10.1126\/science.abl5197.","DOI":"10.1126\/science.abl5197"},{"issue":"10","key":"593_CR12","doi-asserted-by":"publisher","first-page":"852","DOI":"10.1038\/s42256-022-00534-z","volume":"4","author":"F Yang","year":"2022","unstructured":"Yang F, Wang W, Wang F, Fang Y, Tang D, Huang J, et al. scBERT as a large-scale pretrained deep language model for cell type annotation of single-cell RNA-seq data. Nat Mach Intell. 2022;4(10):852\u201366. https:\/\/doi.org\/10.1038\/s42256-022-00534-z.","journal-title":"Nat Mach Intell"},{"key":"593_CR13","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1016\/j.ymeth.2024.05.007","volume":"228","author":"Y Xia","year":"2024","unstructured":"Xia Y, Liu Y, Li T, He S, Chang H, Wang Y, et al. Assessing parameter efficient methods for pre-trained language model in annotating scRNA-seq data. Methods. 2024;228:12\u201321. https:\/\/doi.org\/10.1016\/j.ymeth.2024.05.007.","journal-title":"Methods"},{"key":"593_CR14","doi-asserted-by":"publisher","unstructured":"Li R, Zhang J, Li Z. EasyCellType: marker-based cell-type annotation by automatically querying multiple databases. Bioinf Adv. 2023;3(1):vbad029. https:\/\/doi.org\/10.1093\/bioadv\/vbad029.","DOI":"10.1093\/bioadv\/vbad029"},{"key":"593_CR15","doi-asserted-by":"publisher","unstructured":"Lin A, Ye J, Qi C, Zhu L, Mou W, Gan W, et al. Bridging artificial intelligence and biological sciences: a comprehensive review of large language models in bioinformatics. Briefings Bioinf. 2025;26(4):bbaf357. https:\/\/doi.org\/10.1093\/bib\/bbaf357.","DOI":"10.1093\/bib\/bbaf357"},{"issue":"8","key":"593_CR16","doi-asserted-by":"publisher","first-page":"1462","DOI":"10.1038\/s41592-024-02235-4","volume":"21","author":"W Hou","year":"2024","unstructured":"Hou W, Ji Z. Assessing GPT-4 for cell type annotation in single-cell RNA-seq analysis. Nat Methods. 2024;21(8):1462\u201365. https:\/\/doi.org\/10.1038\/s41592-024-02235-4.","journal-title":"Nat Methods"},{"issue":"1","key":"593_CR17","doi-asserted-by":"publisher","first-page":"1360","DOI":"10.1038\/s42003-025-08745-x","volume":"8","author":"W Ye","year":"2025","unstructured":"Ye W, Ma Y, Xiang J, Liang H, Luo J, Li Y, et al. Evaluation of cell type annotation reliability using a large language model-based identifier. Commun Biol. 2025;8(1):1360. https:\/\/doi.org\/10.1038\/s42003-025-08745-x.","journal-title":"Commun Biol"},{"key":"593_CR18","doi-asserted-by":"publisher","unstructured":"Yang C, Zhang X, Chen J. Large language Model consensus substantially improves the cell type annotation accuracy for scRNA-seq data. bioRxiv. 2025;2025\u201304. https:\/\/doi.org\/10.1101\/2025.04.10.647852.","DOI":"10.1101\/2025.04.10.647852"},{"key":"593_CR19","doi-asserted-by":"publisher","unstructured":"Xie E, Cheng L, Shireman J, Cai Y, Liu J, Mohanty C, et al. CASSIA: a multi-agent large language model for automated and interpretable cell annotation. Nat Commun. 2025;17(1). https:\/\/doi.org\/10.1038\/s41467-025-67084-x.","DOI":"10.1038\/s41467-025-67084-x"},{"issue":"1","key":"593_CR20","doi-asserted-by":"publisher","first-page":"9511","DOI":"10.1038\/s41467-025-64511-x","volume":"16","author":"G Crowley","year":"2025","unstructured":"Crowley G, Quake SR, Krasnow M, Pisco AO, Salzman J, Yosef N, et al. Benchmarking cell type and gene set annotation by large language models with AnnDictionary. Nat Commun. 2025;16(1):9511. https:\/\/doi.org\/10.1038\/s41467-025-64511-x.","journal-title":"Nat Commun"},{"issue":"1","key":"593_CR21","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1186\/s40364-023-00523-3","volume":"11","author":"FL Meng","year":"2023","unstructured":"Meng FL, Huang XL, Qin WY, Liu KB, Wang Y, Li M, et al. singleCellbase: a high-quality manually curated database of cell markers for single cell annotation across multiple species. Biomark Res. 2023;11(1):83. https:\/\/doi.org\/10.1186\/s40364-023-00523-3.","journal-title":"Biomark Res"},{"key":"593_CR22","doi-asserted-by":"publisher","unstructured":"Hu C, Li T, Xu Y, Zhang X, Li F, Bai J, et al. CellMarker 2.0: an updated database of manually curated cell markers in human\/mouse and web tools based on scRNA-seq data. Nucleic Acids Res. 2023;51(D1):D870\u201376. https:\/\/doi.org\/10.1093\/nar\/gkac947.","DOI":"10.1093\/nar\/gkac947"},{"issue":"1","key":"593_CR23","doi-asserted-by":"publisher","first-page":"21183","DOI":"10.1038\/s41598-024-72204-6","volume":"14","author":"P Cheng","year":"2024","unstructured":"Cheng P, Peng Y, Zhang XL, Chen S, Fang BB, Li YZ, et al. A natural language processing system for the efficient extraction of cell markers. Sci Rep. 2024;14(1):21183. https:\/\/doi.org\/10.1038\/s41598-024-72204-6.","journal-title":"Sci Rep"},{"issue":"101","key":"593_CR24","doi-asserted-by":"publisher","first-page":"4371","DOI":"10.21105\/joss.04371","volume":"9","author":"I Virshup","year":"2024","unstructured":"Virshup I, Rybakov S, Theis FJ, Angerer P, Wolf FA. Anndata: access and store annotated datamatrices. J Open Source Softw. 2024;9(101):4371. https:\/\/doi.org\/10.21105\/joss.04371.","journal-title":"JOSS"},{"issue":"1","key":"593_CR25","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1186\/s13059-024-03183-0","volume":"25","author":"JM Pullin","year":"2024","unstructured":"Pullin JM, McCarthy DJ. A comparison of marker gene selection methods for single-cell RNA sequencing data. Genome Biol. 2024;25(1):56. https:\/\/doi.org\/10.1186\/s13059-024-03183-0.","journal-title":"Genome Biol"},{"issue":"1","key":"593_CR26","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1186\/s13059-017-1382-0","volume":"19","author":"FA Wolf","year":"2018","unstructured":"Wolf FA, Angerer P, Theis FJ. SCANPY: large-scale single-cell gene expression data analysis. Genome Biol. 2018;19(1):15. https:\/\/doi.org\/10.1186\/s13059-017-1382-0.","journal-title":"Genome Biol"},{"issue":"8094","key":"593_CR27","doi-asserted-by":"publisher","first-page":"696","DOI":"10.1038\/s41586-025-09686-5","volume":"648","author":"Q Gong","year":"2025","unstructured":"Gong Q, Sharma M, Glass MC, Kuan EL, Chander A, Singh M, et al. Multi-omic profiling reveals age-related immune dynamics in healthy adults. Nature. 2025;648(8094):696\u2013706. https:\/\/doi.org\/10.1038\/s41586-025-09686-5.","journal-title":"Nature"},{"issue":"7860","key":"593_CR28","doi-asserted-by":"publisher","first-page":"575","DOI":"10.1038\/s41586-021-03549-5","volume":"593","author":"MB Buechler","year":"2021","unstructured":"Buechler MB, Pradhan RN, Krishnamurty AT, Cox C, Calviello AK, Wang AW, et al. Cross-tissue organization of the fibroblast lineage. Nature. 2021;593(7860):575\u201379. https:\/\/doi.org\/10.1038\/s41586-021-03549-5.","journal-title":"Nature"},{"issue":"15","key":"593_CR29","doi-asserted-by":"publisher","first-page":"1970","DOI":"10.3390\/cells12151970","volume":"12","author":"C Cheng","year":"2023","unstructured":"Cheng C, Chen W, Jin H, Chen X. A review of single-cell rna-seq annotation, integration, and cell\u2013cell communication. Cells. 2023;12(15):1970. https:\/\/doi.org\/10.3390\/cells12151970.","journal-title":"Cells"},{"key":"593_CR30","doi-asserted-by":"publisher","unstructured":"Miller JA, Gouwens NW, Tasic B, Collman F, van Velthoven CT, Bakken TE, et al. Common cell type nomenclature for the mammalian brain. Elife. 2020;9:e59928. https:\/\/doi.org\/10.7554\/eLife.59928.","DOI":"10.7554\/eLife.59928"}],"container-title":["BioData Mining"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s13040-026-00593-7","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13040-026-00593-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13040-026-00593-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,9]],"date-time":"2026-09-09T14:44:17Z","timestamp":1788965057000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1186\/s13040-026-00593-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8,11]]},"references-count":30,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["593"],"URL":"https:\/\/doi.org\/10.1186\/s13040-026-00593-7","relation":{},"ISSN":["1756-0381"],"issn-type":[{"value":"1756-0381","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,8,11]]},"assertion":[{"value":"10 March 2026","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 August 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 August 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"Not applicable.","order":1,"name":"Ethics","label":"Ethics approval and consent to participate","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","label":"Consent for publication","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":3,"name":"Ethics","label":"Competing interests","group":{"name":"EthicsHeading","label":"Declarations"}}],"article-number":"76"}}