{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,25]],"date-time":"2025-02-25T05:35:55Z","timestamp":1740461755357,"version":"3.37.3"},"reference-count":29,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,2,25]],"date-time":"2025-02-25T00:00:00Z","timestamp":1740441600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,2,25]],"date-time":"2025-02-25T00:00:00Z","timestamp":1740441600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"name":"the Key Laboratory of Maritime Intelligent Cyberspace Technology (Hohai University), Ministry of Education, China","award":["B240203012"],"award-info":[{"award-number":["B240203012"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Bioinformatics"],"DOI":"10.1186\/s12859-025-06061-z","type":"journal-article","created":{"date-parts":[[2025,2,25]],"date-time":"2025-02-25T02:58:46Z","timestamp":1740452326000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["scFTAT: a novel cell annotation method integrating FFT and transformer"],"prefix":"10.1186","volume":"26","author":[{"given":"Binhua","family":"Tang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yiyao","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,2,25]]},"reference":[{"issue":"12","key":"6061_CR1","doi-asserted-by":"publisher","first-page":"972","DOI":"10.20892\/j.issn.2095-3941.2023.0436","volume":"20","author":"X Shen","year":"2024","unstructured":"Shen X, Li X. Deep-learning methods for unveiling large-scale single-cell transcriptomes. Cancer Biol Med. 2024;20(12):972\u201380.","journal-title":"Cancer Biol Med"},{"issue":"1","key":"6061_CR2","doi-asserted-by":"publisher","first-page":"0531","DOI":"10.1093\/bib\/bbab531","volume":"23","author":"M Flores","year":"2022","unstructured":"Flores M, et al. Deep learning tackles single-cell analysis-a survey of deep learning for scRNA-seq analysis. Brief Bioinf. 2022;23(1):0531.","journal-title":"Brief Bioinf"},{"key":"6061_CR3","doi-asserted-by":"publisher","first-page":"961","DOI":"10.1016\/j.csbj.2021.01.015","volume":"19","author":"G Pasquini","year":"2021","unstructured":"Pasquini G, et al. Automated methods for cell type annotation on scRNA-seq data. Comput Struct Biotechnol J. 2021;19:961\u20139.","journal-title":"Comput Struct Biotechnol J"},{"issue":"1","key":"6061_CR4","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1186\/s13059-021-02281-7","volume":"22","author":"H Guo","year":"2021","unstructured":"Guo H, Li J. scSorter: assigning cells to known cell types according to marker genes. Genome Biol. 2021;22(1):69.","journal-title":"Genome Biol"},{"issue":"2","key":"6061_CR5","doi-asserted-by":"publisher","first-page":"267","DOI":"10.1016\/j.gpb.2020.07.004","volume":"19","author":"Q Huang","year":"2021","unstructured":"Huang Q, et al. Evaluation of cell type annotation R packages on single-cell RNA-seq data. Genomics Proteom Bioinf. 2021;19(2):267\u201381.","journal-title":"Genomics Proteom Bioinf"},{"issue":"5","key":"6061_CR6","doi-asserted-by":"publisher","first-page":"411","DOI":"10.1038\/nbt.4096","volume":"36","author":"A Butler","year":"2018","unstructured":"Butler A, et al. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat Biotechnol. 2018;36(5):411\u201320.","journal-title":"Nat Biotechnol"},{"issue":"1","key":"6061_CR7","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.","journal-title":"Genome Biol"},{"issue":"2","key":"6061_CR8","doi-asserted-by":"publisher","first-page":"163","DOI":"10.1038\/s41590-018-0276-y","volume":"20","author":"D Aran","year":"2019","unstructured":"Aran D, et al. Reference-based analysis of lung single-cell sequencing reveals a transitional profibrotic macrophage. Nat Immunol. 2019;20(2):163\u201372.","journal-title":"Nat Immunol"},{"issue":"5","key":"6061_CR9","doi-asserted-by":"publisher","first-page":"421","DOI":"10.1038\/nbt.4091","volume":"36","author":"L Haghverdi","year":"2018","unstructured":"Haghverdi L, et al. Batch effects in single-cell RNA-sequencing data are corrected by matching mutual nearest neighbors. Nat Biotechnol. 2018;36(5):421\u20137.","journal-title":"Nat Biotechnol"},{"issue":"16","key":"6061_CR10","doi-asserted-by":"publisher","first-page":"4415","DOI":"10.1093\/bioinformatics\/btaa293","volume":"36","author":"CH Gr\u00f8nbech","year":"2020","unstructured":"Gr\u00f8nbech CH, et al. scVAE: variational auto-encoders for single-cell gene expression data. Bioinformatics. 2020;36(16):4415\u201322.","journal-title":"Bioinformatics"},{"issue":"1","key":"6061_CR11","doi-asserted-by":"publisher","first-page":"1882","DOI":"10.1038\/s41467-021-22197-x","volume":"12","author":"J Wang","year":"2021","unstructured":"Wang J, et al. scGNN is a novel graph neural network framework for single-cell RNA-Seq analyses. Nat Commun. 2021;12(1):1882.","journal-title":"Nat Commun"},{"key":"6061_CR12","unstructured":"Vaswani A, et al. Attention is all you need. In: NIPS. 2017."},{"issue":"1","key":"6061_CR13","doi-asserted-by":"publisher","first-page":"390","DOI":"10.1038\/s41467-018-07931-2","volume":"10","author":"G Eraslan","year":"2019","unstructured":"Eraslan G, et al. Single-cell RNA-seq denoising using a deep count autoencoder. Nat Commun. 2019;10(1):390.","journal-title":"Nat Commun"},{"issue":"2","key":"6061_CR14","doi-asserted-by":"publisher","first-page":"0039","DOI":"10.1093\/nargab\/lqaa039","volume":"2","author":"L Chen","year":"2020","unstructured":"Chen L, et al. Deep soft K-means clustering with self-training for single-cell RNA sequence data. Nar Genomics Bioinf. 2020;2(2):0039.","journal-title":"Nar Genomics Bioinf"},{"issue":"4","key":"6061_CR15","doi-asserted-by":"publisher","first-page":"191","DOI":"10.1038\/s42256-019-0037-0","volume":"1","author":"T Tian","year":"2019","unstructured":"Tian T, et al. Clustering single-cell RNA-seq data with a model-based deep learning approach. Nat Mach Intell. 2019;1(4):191\u20138.","journal-title":"Nat Mach Intell"},{"issue":"12","key":"6061_CR16","doi-asserted-by":"publisher","first-page":"1053","DOI":"10.1038\/s41592-018-0229-2","volume":"15","author":"R Lopez","year":"2018","unstructured":"Lopez R, et al. Deep generative modeling for single-cell transcriptomics. Nat Methods. 2018;15(12):1053\u20138.","journal-title":"Nat Methods"},{"issue":"2","key":"6061_CR17","doi-asserted-by":"publisher","first-page":"bbac018","DOI":"10.1093\/bib\/bbac018","volume":"23","author":"Y Gan","year":"2022","unstructured":"Gan Y, et al. Deep structural clustering for single-cell RNA-seq data jointly through autoencoder and graph neural network. Brief Bioinf. 2022;23(2):bbac018.","journal-title":"Brief Bioinf"},{"issue":"1","key":"6061_CR18","doi-asserted-by":"publisher","first-page":"264","DOI":"10.1186\/s13059-019-1862-5","volume":"20","author":"J Alquicira-Hernandez","year":"2019","unstructured":"Alquicira-Hernandez J, et al. scPred: accurate supervised method for cell-type classification from single-cell RNA-seq data. Genome Biol. 2019;20(1):264.","journal-title":"Genome Biol"},{"issue":"1","key":"6061_CR19","doi-asserted-by":"publisher","first-page":"1169","DOI":"10.1038\/s41467-020-14976-9","volume":"11","author":"P Qiu","year":"2020","unstructured":"Qiu P. Embracing the dropouts in single-cell RNA-seq analysis. Nat Commun. 2020;11(1):1169.","journal-title":"Nat Commun"},{"issue":"4","key":"6061_CR20","doi-asserted-by":"publisher","first-page":"0195","DOI":"10.1093\/bib\/bbad195","volume":"24","author":"J Xu","year":"2023","unstructured":"Xu J, et al. CIForm as a transformer-based model for cell-type annotation of large-scale single-cell RNA-seq data. Brief Bioinf. 2023;24(4):0195.","journal-title":"Brief Bioinf"},{"key":"6061_CR21","doi-asserted-by":"crossref","unstructured":"Liu Y, et al., A survey of visual transformers. IEEE Trans Neural Netw Learn Syst. 2023;1\u201321.","DOI":"10.1109\/TNNLS.2024.3476068"},{"issue":"21","key":"6061_CR22","doi-asserted-by":"publisher","DOI":"10.1093\/nar\/gkab775","volume":"49","author":"X Shao","year":"2021","unstructured":"Shao X, et al. scDeepSort: a pre-trained cell-type annotation method for single-cell transcriptomics using deep learning with a weighted graph neural network. Nucleic Acids Res. 2021;49(21): e122.","journal-title":"Nucleic Acids Res"},{"key":"6061_CR23","doi-asserted-by":"publisher","first-page":"1038919","DOI":"10.3389\/fgene.2022.1038919","volume":"13","author":"T Song","year":"2022","unstructured":"Song T, et al. TransCluster: a cell-type identification method for single-cell RNA-Seq data using deep learning based on transformer. Front Genet. 2022;13:1038919.","journal-title":"Front Genet"},{"issue":"9","key":"6061_CR24","doi-asserted-by":"publisher","first-page":"10960","DOI":"10.1109\/TPAMI.2023.3263824","volume":"45","author":"Y Rao","year":"2023","unstructured":"Rao Y, et al. GFNet: global filter networks for visual recognition. IEEE Trans Pattern Anal Mach Intell. 2023;45(9):10960\u201373.","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"6061_CR25","doi-asserted-by":"publisher","DOI":"10.7717\/peerj-cs.1344","volume":"9","author":"S Liu","year":"2023","unstructured":"Liu S, et al. Image classification model based on large kernel attention mechanism and relative position self-attention mechanism. PeerJ Comput Sci. 2023;9: e1344.","journal-title":"PeerJ Comput Sci"},{"key":"6061_CR26","unstructured":"Katharopoulos A, et al., Transformers are RNNs: fast autoregressive transformers with linear attention. In: D Hal, III, S Aarti (eds) Proceedings of the 37th international conference on machine learning, 2020, PMLR: Proceedings of Machine Learning Research. p. 5156\u20135165."},{"key":"6061_CR27","unstructured":"Bachlechner TC, et al. ReZero is all you need: fast convergence at large depth. In: Conference on uncertainty in artificial intelligence. 2020."},{"issue":"8","key":"6061_CR28","doi-asserted-by":"publisher","first-page":"2011","DOI":"10.1109\/TPAMI.2019.2913372","volume":"42","author":"J Hu","year":"2020","unstructured":"Hu J, et al. Squeeze-and-excitation networks. IEEE Trans Pattern Anal Mach Intell. 2020;42(8):2011\u201323.","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"6061_CR29","doi-asserted-by":"crossref","unstructured":"Liu T, et al., Evaluating the utilities of foundation models in single-cell data analysis. bioRxiv. 2024.","DOI":"10.1101\/2023.09.08.555192"}],"container-title":["BMC Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12859-025-06061-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12859-025-06061-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12859-025-06061-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,2,25]],"date-time":"2025-02-25T02:58:53Z","timestamp":1740452333000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcbioinformatics.biomedcentral.com\/articles\/10.1186\/s12859-025-06061-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,2,25]]},"references-count":29,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["6061"],"URL":"https:\/\/doi.org\/10.1186\/s12859-025-06061-z","relation":{},"ISSN":["1471-2105"],"issn-type":[{"value":"1471-2105","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,2,25]]},"assertion":[{"value":"7 October 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 January 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 February 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 and consent to participate"}},{"value":"Not applicable.","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":"62"}}