{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,2]],"date-time":"2026-08-02T17:45:10Z","timestamp":1785692710068,"version":"3.56.0"},"publisher-location":"Cham","reference-count":20,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032343765","type":"print"},{"value":"9783032343772","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,8,3]],"date-time":"2026-08-03T00:00:00Z","timestamp":1785715200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,8,3]],"date-time":"2026-08-03T00:00:00Z","timestamp":1785715200000},"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":[],"published-print":{"date-parts":[[2027]]},"DOI":"10.1007\/978-3-032-34377-2_19","type":"book-chapter","created":{"date-parts":[[2026,8,2]],"date-time":"2026-08-02T17:40:03Z","timestamp":1785692403000},"page":"213-228","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Deep Learning Techniques to\u00a0Enhance Multimodal Single Cell Analysis"],"prefix":"10.1007","author":[{"given":"Ming","family":"Yu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7347-3501","authenticated-orcid":false,"given":"Wei","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fang","family":"Lu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3109-5762","authenticated-orcid":false,"given":"Dongyun","family":"Nie","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,8,3]]},"reference":[{"key":"19_CR1","doi-asserted-by":"publisher","first-page":"101255","DOI":"10.1016\/j.mam.2024.101255","volume":"96","author":"CU Yuan","year":"2024","unstructured":"Yuan, C.U., Quah, F.X., Hemberg, M.: Single-cell and spatial transcriptomics: bridging current technologies with long-read sequencing. Mol. Aspects Med. 96, 101255 (2024)","journal-title":"Mol. Aspects Med."},{"key":"19_CR2","doi-asserted-by":"crossref","unstructured":"Li, S., Hua, H., Chen, S.: Graph neural networks for single-cell omics data: a review of approaches and applications. Briefings Bioinf. 26(2), (2025)","DOI":"10.1093\/bib\/bbaf109"},{"key":"19_CR3","unstructured":"Alberts, B., et al.: Molecular Biology of the Cell, 5th ed. Garland Science, New York, ch. 8, p. 550 (2008)"},{"key":"19_CR4","doi-asserted-by":"publisher","first-page":"377","DOI":"10.1038\/nmeth.1315","volume":"6","author":"F Tang","year":"2009","unstructured":"Tang, F., et al.: mRNA-seq whole-transcriptome analysis of a single cell. Nat. Methods 6, 377\u2013382 (2009)","journal-title":"Nat. Methods"},{"key":"19_CR5","unstructured":"10x Genomics, Chromium Single Cell Multiome ATAC + Gene Expression User Guide. https:\/\/www.10xgenomics.com (2020)"},{"issue":"2","key":"19_CR6","doi-asserted-by":"publisher","first-page":"28","DOI":"10.3390\/mps4020028","volume":"4","author":"D Mercatelli","year":"2021","unstructured":"Mercatelli, D., et al.: The transcriptome of SH-SY5Y at single-cell resolution: a CITE-Seq data analysis workflow. Methods Protoc. 4(2), 28 (2021)","journal-title":"Methods Protoc."},{"key":"19_CR7","doi-asserted-by":"publisher","first-page":"940","DOI":"10.1038\/s42256-022-00545-w","volume":"4","author":"J Lakkis","year":"2022","unstructured":"Lakkis, J., et al.: A multi-use deep learning method for CITE-seq and single-cell RNA-seq data integration with cell surface protein prediction and imputation. Nat. Mach. Intell. 4, 940\u2013952 (2022)","journal-title":"Nat. Mach. Intell."},{"key":"19_CR8","doi-asserted-by":"publisher","first-page":"651","DOI":"10.1038\/s41467-020-14391-0","volume":"11","author":"Z Zhou","year":"2020","unstructured":"Zhou, Z., et al.: Surface protein imputation from single cell transcriptomes by deep neural networks. Nat. Commun. 11, 651 (2020)","journal-title":"Nat. Commun."},{"issue":"9","key":"19_CR9","doi-asserted-by":"publisher","first-page":"875","DOI":"10.1038\/s41592-019-0537-1","volume":"16","author":"J Wang","year":"2019","unstructured":"Wang, J., et al.: Data denoising with transfer learning in single-cell transcriptomics. Nat. Methods 16(9), 875\u2013878 (2019)","journal-title":"Nat. Methods"},{"issue":"Suppl 6","key":"19_CR10","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1186\/s12859-021-04022-w","volume":"22","author":"X Dai","year":"2021","unstructured":"Dai, X., et al.: PIKE-R2P: Protein-protein interaction network-based knowledge embedding with graph neural network for single-cell RNA to protein prediction. BMC Bioinformatics 22(Suppl 6), 139 (2021)","journal-title":"BMC Bioinformatics"},{"key":"19_CR11","doi-asserted-by":"publisher","first-page":"940","DOI":"10.1038\/s42256-022-00545-w","volume":"4","author":"J Lakkis","year":"2022","unstructured":"Lakkis, J., et al.: A multi-use deep learning method for CITE-seq and single-cell RNA-seq data integration. Nat. Mach. Intell. 4, 940\u2013952 (2022)","journal-title":"Nat. Mach. Intell."},{"issue":"51","key":"19_CR12","doi-asserted-by":"publisher","first-page":"21521","DOI":"10.1073\/pnas.0904863106","volume":"106","author":"Z Ouyang","year":"2009","unstructured":"Ouyang, Z., Zhou, Q., Wong, W.H.: ChIP-seq of transcription factors predicts absolute and differential gene expression in embryonic stem cells. PNAS 106(51), 21521\u201321526 (2009)","journal-title":"PNAS"},{"issue":"7","key":"19_CR13","doi-asserted-by":"publisher","first-page":"107663","DOI":"10.1016\/j.celrep.2020.107663","volume":"31","author":"V Agarwal","year":"2020","unstructured":"Agarwal, V., Shendure, J.: Predicting mRNA abundance directly from genomic sequence using deep convolutional neural networks. Cell Rep. 31(7), 107663 (2020)","journal-title":"Cell Rep."},{"key":"19_CR14","doi-asserted-by":"publisher","first-page":"443","DOI":"10.1186\/s12859-023-05560-1","volume":"24","author":"E Pianfetti","year":"2023","unstructured":"Pianfetti, E., et al.: MiREx: mRNA levels prediction from gene sequence and miRNA target knowledge. BMC Bioinformatics 24, 443 (2023)","journal-title":"BMC Bioinformatics"},{"key":"19_CR15","doi-asserted-by":"publisher","first-page":"583","DOI":"10.1038\/s41586-021-03819-2","volume":"596","author":"J Jumper","year":"2021","unstructured":"Jumper, J., et al.: Highly accurate protein structure prediction with AlphaFold. Nature 596, 583\u2013589 (2021)","journal-title":"Nature"},{"key":"19_CR16","doi-asserted-by":"crossref","unstructured":"Nambiar, A.: Transforming the language of life: transformer neural networks for protein prediction tasks. bioRxiv (2020)","DOI":"10.1101\/2020.06.15.153643"},{"key":"19_CR17","unstructured":"Burkhardt, D.: Open Problems - Multimodal Single-Cell Integration, Kaggle (2022)"},{"key":"19_CR18","doi-asserted-by":"crossref","unstructured":"Rajaraman, A., Ullman, J.D.: Data mining, in mining of massive datasets (2011)","DOI":"10.1017\/CBO9781139058452"},{"key":"19_CR19","doi-asserted-by":"crossref","unstructured":"Hilt, D.E., Seegrist, D.W.: Ridge, a computer program for calculating ridge regression estimates (1977)","DOI":"10.5962\/bhl.title.68934"},{"issue":"2","key":"19_CR20","doi-asserted-by":"publisher","first-page":"164","DOI":"10.1109\/TAC.1980.1102314","volume":"25","author":"V Klema","year":"1980","unstructured":"Klema, V., Laub, A.: The singular value decomposition: its computation and some applications. IEEE Trans. Autom. Control 25(2), 164\u2013176 (1980)","journal-title":"IEEE Trans. Autom. Control"}],"container-title":["Lecture Notes in Computer Science","Database and Expert Systems Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-34377-2_19","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,2]],"date-time":"2026-08-02T17:40:04Z","timestamp":1785692404000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-34377-2_19"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8,3]]},"ISBN":["9783032343765","9783032343772"],"references-count":20,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-34377-2_19","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,8,3]]},"assertion":[{"value":"3 August 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"DEXA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Database and Expert Systems Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Graz","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Austria","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2026","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11 August 2026","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13 August 2026","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"37","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"dexa2026","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.dexa.org\/2026\/dexa2026.html","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}