{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,22]],"date-time":"2026-07-22T03:33:19Z","timestamp":1784691199138,"version":"3.55.0"},"reference-count":92,"publisher":"Springer Science and Business Media LLC","issue":"17","license":[{"start":{"date-parts":[[2024,4,17]],"date-time":"2024-04-17T00:00:00Z","timestamp":1713312000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,4,17]],"date-time":"2024-04-17T00:00:00Z","timestamp":1713312000000},"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":["Neural Comput & Applic"],"published-print":{"date-parts":[[2024,6]]},"DOI":"10.1007\/s00521-024-09662-6","type":"journal-article","created":{"date-parts":[[2024,4,17]],"date-time":"2024-04-17T10:22:15Z","timestamp":1713349335000},"page":"9963-9987","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["Graph neural network approaches for single-cell data: a recent overview"],"prefix":"10.1007","volume":"36","author":[{"given":"Konstantinos","family":"Lazaros","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dimitris E.","family":"Koumadorakis","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Panagiotis","family":"Vlamos","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aristidis G.","family":"Vrahatis","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,4,17]]},"reference":[{"key":"9662_CR1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13578-019-0314-y","volume":"9","author":"X Tang","year":"2019","unstructured":"Tang X, Huang Y, Lei J, Luo H, Zhu X (2019) The single-cell sequencing: new developments and medical applications. Cell Biosci 9:1\u20139","journal-title":"Cell Biosci"},{"issue":"7871","key":"9662_CR2","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1038\/s41586-021-03634-9","volume":"596","author":"A Rao","year":"2021","unstructured":"Rao A, Barkley D, Fran\u00e7a GS, Yanai I (2021) Exploring tissue architecture using spatial transcriptomics. Nature 596(7871):211\u2013220","journal-title":"Nature"},{"issue":"4","key":"9662_CR3","doi-asserted-by":"crossref","first-page":"493","DOI":"10.1161\/ATVBAHA.122.318731","volume":"43","author":"R-S Wang","year":"2023","unstructured":"Wang R-S, Maron BA, Loscalzo J (2023) Multiomics network medicine approaches to precision medicine and therapeutics in cardiovascular diseases. Arterioscler Thromb Vasc Biol 43(4):493\u2013503","journal-title":"Arterioscler Thromb Vasc Biol"},{"issue":"4","key":"9662_CR4","doi-asserted-by":"crossref","first-page":"379","DOI":"10.1042\/ETLS20180176","volume":"3","author":"M Blencowe","year":"2019","unstructured":"Blencowe M, Arneson D, Ding J, Chen Y-W, Saleem Z, Yang X (2019) Network modeling of single-cell omics data: challenges, opportunities, and progresses. Emerg Topics Life Sci 3(4):379\u2013398","journal-title":"Emerg Topics Life Sci"},{"key":"9662_CR5","doi-asserted-by":"publisher","unstructured":"Wu, L., Cui, P., Pei, J., Zhao, L., Song, L.: (2022) Graph neural networks. Springer, Singapore. pp. 27\u201337. https:\/\/doi.org\/10.1007\/978-981-16-6054-2_3","DOI":"10.1007\/978-981-16-6054-2_3"},{"key":"9662_CR6","doi-asserted-by":"crossref","first-page":"22106","DOI":"10.1109\/ACCESS.2021.3055280","volume":"9","author":"S Georgousis","year":"2021","unstructured":"Georgousis S, Kenning MP, Xie X (2021) Graph deep learning: state of the art and challenges. IEEE Access 9:22106\u201322140","journal-title":"IEEE Access"},{"key":"9662_CR7","doi-asserted-by":"crossref","first-page":"6303","DOI":"10.1109\/TSP.2020.3033962","volume":"68","author":"L Ruiz","year":"2020","unstructured":"Ruiz L, Gama F, Ribeiro A (2020) Gated graph recurrent neural networks. IEEE Trans Signal Process 68:6303\u20136318","journal-title":"IEEE Trans Signal Process"},{"key":"9662_CR8","unstructured":"Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016)"},{"key":"9662_CR9","unstructured":"Kipf, T.N., Welling, M.: Variational graph auto-encoders. arXiv preprint arXiv:1611.07308 (2016)"},{"key":"9662_CR10","unstructured":"Mingshuo, N., Dongming, C., Dongqi, W.: Reinforcement learning on graph: A survey. arXiv e-prints, 2204 (2022)"},{"key":"9662_CR11","unstructured":"Chen, L., Li, J., Peng, J., Xie, T., Cao, Z., Xu, K., He, X., Zheng, Z., Wu, B.: A survey of adversarial learning on graphs. arXiv preprint arXiv:2003.05730 (2020)"},{"key":"9662_CR12","unstructured":"Veli\u010dkovi\u0107, P., Cucurull, G., Casanova, A., Romero, A., Lio, P., Bengio, Y.: Graph attention networks. arXiv preprint arXiv:1710.10903 (2017)"},{"key":"9662_CR13","unstructured":"Labonne, M.: Hands-On Graph Neural Networks Using Python. Packt, ??? (2023)"},{"issue":"1","key":"9662_CR14","doi-asserted-by":"crossref","first-page":"1882","DOI":"10.1038\/s41467-021-22197-x","volume":"12","author":"J Wang","year":"2021","unstructured":"Wang J, Ma A, Chang Y, Gong J, Jiang Y, Qi R, Wang C, Fu H, Ma Q, Xu D (2021) scgnn is a novel graph neural network framework for single-cell rna-seq analyses. Nature commun 12(1):1882","journal-title":"Nature commun"},{"issue":"21","key":"9662_CR15","doi-asserted-by":"crossref","first-page":"122","DOI":"10.1093\/nar\/gkab775","volume":"49","author":"X Shao","year":"2021","unstructured":"Shao X, Yang H, Zhuang X, Liao J, Yang P, Cheng J, Lu X, Chen H, Fan X (2021) scdeepsort: a pre-trained cell-type annotation method for single-cell transcriptomics using deep learning with a weighted graph neural network. Nucleic acids research 49(21):122\u2013122","journal-title":"Nucleic acids research"},{"key":"9662_CR16","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13059-019-1906-x","volume":"21","author":"W Hou","year":"2020","unstructured":"Hou W, Ji Z, Ji H, Hicks SC (2020) A systematic evaluation of single-cell rna-sequencing imputation methods. Genome biology 21:1\u201330","journal-title":"Genome biology"},{"key":"9662_CR17","doi-asserted-by":"crossref","unstructured":"Rao, J., Zhou, X., Lu, Y., Zhao, H., Yang, Y.: Imputing single-cell rna-seq data by combining graph convolution and autoencoder neural networks. iscience (2021)","DOI":"10.1101\/2020.02.05.935296"},{"key":"9662_CR18","doi-asserted-by":"crossref","first-page":"1003711","DOI":"10.3389\/fgene.2022.1003711","volume":"13","author":"X Feng","year":"2022","unstructured":"Feng X, Fang F, Long H, Zeng R, Yao Y (2022) Single-cell rna-seq data analysis using graph autoencoders and graph attention networks. Fronti Genet 13:1003711","journal-title":"Fronti Genet"},{"key":"9662_CR19","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12859-020-03881-z","volume":"22","author":"C Xu","year":"2021","unstructured":"Xu C, Cai L, Gao J (2021) An efficient scrna-seq dropout imputation method using graph attention network. BMC Bioinform 22:1\u201318","journal-title":"BMC Bioinform"},{"key":"9662_CR20","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1016\/j.ymeth.2023.02.008","volume":"211","author":"X Feng","year":"2023","unstructured":"Feng X, Zhang H, Lin H, Long H (2023) Single-cell rna-seq data analysis based on directed graph neural network. Methods 211:48\u201360","journal-title":"Methods"},{"issue":"23","key":"9662_CR21","doi-asserted-by":"crossref","first-page":"5322","DOI":"10.1093\/bioinformatics\/btac684","volume":"38","author":"H Gu","year":"2022","unstructured":"Gu H, Cheng H, Ma A, Li Y, Wang J, Xu D, Ma Q (2022) scgnn 20: a graph neural network tool for imputation and clustering of single-cell rna-seq data. Bioinformatics 38(23):5322\u20135325","journal-title":"Bioinformatics"},{"issue":"5","key":"9662_CR22","doi-asserted-by":"crossref","first-page":"313","DOI":"10.1093\/bib\/bbac313","volume":"23","author":"X Wu","year":"2022","unstructured":"Wu X, Zhou Y (2022) Ge-impute: graph embedding-based imputation for single-cell rna-seq data. Brief Bioinform 23(5):313","journal-title":"Brief Bioinform"},{"issue":"19","key":"9662_CR23","doi-asserted-by":"crossref","first-page":"4522","DOI":"10.1093\/bioinformatics\/btac559","volume":"38","author":"G Chen","year":"2022","unstructured":"Chen G, Liu Z-P (2022) Graph attention network for link prediction of gene regulations from single-cell rna-sequencing data. Bioinformatics 38(19):4522\u20134529","journal-title":"Bioinformatics"},{"issue":"5","key":"9662_CR24","doi-asserted-by":"crossref","first-page":"1277","DOI":"10.1093\/bioinformatics\/btab804","volume":"38","author":"D Buterez","year":"2022","unstructured":"Buterez D, Bica I, Tariq I, Andr\u00e9s-Terr\u00e9 H, Li\u00f2 P (2022) Cellvgae: an unsupervised scrna-seq analysis workflow with graph attention networks. Bioinformatics 38(5):1277\u20131286","journal-title":"Bioinformatics"},{"issue":"18","key":"9662_CR25","doi-asserted-by":"crossref","first-page":"10220","DOI":"10.3390\/ijms231810220","volume":"23","author":"S Baul","year":"2022","unstructured":"Baul S, Ahmed KT, Filipek J, Zhang W (2022) omicsgat: graph attention network for cancer subtype analyses. Int J Mol Sci 23(18):10220","journal-title":"Int J Mol Sci"},{"issue":"8","key":"9662_CR26","doi-asserted-by":"crossref","first-page":"2187","DOI":"10.1093\/bioinformatics\/btac099","volume":"38","author":"Y Cheng","year":"2022","unstructured":"Cheng Y, Ma X (2022) scgac: a graph attentional architecture for clustering single-cell rna-seq data. Bioinformatics 38(8):2187\u20132193","journal-title":"Bioinformatics"},{"issue":"9","key":"9662_CR27","doi-asserted-by":"crossref","first-page":"720","DOI":"10.1016\/j.jgg.2023.06.005","volume":"50","author":"Y Huo","year":"2023","unstructured":"Huo Y, Guo Y, Wang J, Xue H, Feng Y, Chen W, Li X (2023) Integrating multi-modal information to detect spatial domains of spatial transcriptomics by graph attention network. J Genet Genom 50(9):720","journal-title":"J Genet Genom"},{"issue":"1","key":"9662_CR28","doi-asserted-by":"crossref","first-page":"1739","DOI":"10.1038\/s41467-022-29439-6","volume":"13","author":"K Dong","year":"2022","unstructured":"Dong K, Zhang S (2022) Deciphering spatial domains from spatially resolved transcriptomics with an adaptive graph attention auto-encoder. Nature Commun 13(1):1739","journal-title":"Nature Commun"},{"issue":"1","key":"9662_CR29","first-page":"1","volume":"24","author":"SAR Abadi","year":"2023","unstructured":"Abadi SAR, Laghaee SP, Koohi S (2023) An optimized graph-based structure for single-cell rna-seq cell-type classification based on non-linear dimension reduction. BMC Genom 24(1):1\u201313","journal-title":"BMC Genom"},{"issue":"1","key":"9662_CR30","doi-asserted-by":"crossref","first-page":"20028","DOI":"10.1038\/s41598-021-99003-7","volume":"11","author":"Z Luo","year":"2021","unstructured":"Luo Z, Xu C, Zhang Z, Jin W (2021) A topology-preserving dimensionality reduction method for single-cell rna-seq data using graph autoencoder. Sci Rep 11(1):20028","journal-title":"Sci Rep"},{"key":"9662_CR31","volume":"163","author":"S Wang","year":"2023","unstructured":"Wang S, Zhang Y, Zhang Y, Wu W, Ye L, Li Y, Su J, Pang S (2023) scasgc: an adaptive simplified graph convolution model for clustering single-cell rna-seq data. Comput Biol Med 163:107152","journal-title":"Comput Biol Med"},{"key":"9662_CR32","volume":"12","author":"J Zhao","year":"2021","unstructured":"Zhao J, Wang N, Wang H, Zheng C, Su Y (2021) Scdrha: a scrna-seq data dimensionality reduction algorithm based on hierarchical autoencoder. Front Genet 12:733906","journal-title":"Front Genet"},{"key":"9662_CR33","doi-asserted-by":"crossref","unstructured":"So, E., Hayat, S., Kadambat\u00a0Nair, S., Wang, B., Haibe-Kains, B.: (2023) Graphcomm: a graph-based deep learning method to predict cell-cell communication in single-cell rnaseq data. bioRxiv, 2023\u201304","DOI":"10.1101\/2023.04.26.538432"},{"issue":"2","key":"9662_CR34","first-page":"043","volume":"39","author":"K Wang","year":"2023","unstructured":"Wang K, Li Z, You Z-H, Han P, Nie R (2023) Adversarial dense graph convolutional networks for single-cell classification. Bioinformatics 39(2):043","journal-title":"Bioinformatics"},{"key":"9662_CR35","doi-asserted-by":"crossref","unstructured":"Shahir, J.A., Stanley, N., Purvis, J.E.: (2023) Cellograph: a semi-supervised approach to analyzing multi-condition single-cell rna-sequencing data using graph neural networks. bioRxiv, 2023\u201302","DOI":"10.1101\/2023.02.24.528672"},{"issue":"1","key":"9662_CR36","doi-asserted-by":"crossref","first-page":"964","DOI":"10.1038\/s41467-023-36559-0","volume":"14","author":"A Ma","year":"2023","unstructured":"Ma A, Wang X, Li J, Wang C, Xiao T, Liu Y, Cheng H, Wang J, Li Y, Chang Y et al (2023) Single-cell biological network inference using a heterogeneous graph transformer. Nature Commun 14(1):964","journal-title":"Nature Commun"},{"key":"9662_CR37","doi-asserted-by":"crossref","unstructured":"Wang G, Zhao J, Yan Y, Wang Y, Wu AR, Yang C: (2023) Construction of a 3d whole organism spatial atlas by joint modeling of multiple slices. bioRxiv, 2023\u201302","DOI":"10.1101\/2023.02.02.526814"},{"issue":"1","key":"9662_CR38","doi-asserted-by":"crossref","first-page":"563","DOI":"10.1093\/bib\/bbac563","volume":"24","author":"Z Tang","year":"2023","unstructured":"Tang Z, Zhang T, Yang B, Su J, Song Q (2023) spaci: deciphering spatial cellular communications through adaptive graph model. Brief Bioinform 24(1):563","journal-title":"Brief Bioinform"},{"issue":"6","key":"9662_CR39","first-page":"1","volume":"22","author":"X Dai","year":"2021","unstructured":"Dai X, Xu F, Wang S, Mundra PA, Zheng J (2021) Pike-r2p: Protein-protein interaction network-based knowledge embedding with graph neural network for single-cell rna to protein prediction. BMC Bioinform 22(6):1\u201316","journal-title":"BMC Bioinform"},{"key":"9662_CR40","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1016\/j.ins.2022.11.049","volume":"621","author":"Y Shan","year":"2023","unstructured":"Shan Y, Yang J, Li X, Zhong X, Chang Y (2023) Glae: A graph-learnable auto-encoder for single-cell rna-seq analysis. Inform Sci 621:88\u2013103","journal-title":"Inform Sci"},{"key":"9662_CR41","first-page":"4671","volume":"36","author":"Z Yu","year":"2022","unstructured":"Yu Z, Lu Y, Wang Y, Tang F, Wong K-C, Li X (2022) Zinb-based graph embedding autoencoder for single-cell rna-seq interpretations. Proc AAAI Conf Artif Intell 36:4671\u20134679","journal-title":"Proc AAAI Conf Artif Intell"},{"key":"9662_CR42","doi-asserted-by":"crossref","unstructured":"Shao X, Yang H, Zhuang X, Liao J, Yang Y, Yang P, Cheng J, Lu X, Chen H, Fan X: (2020) Reference-free cell-type annotation for single-cell transcriptomics using deep learning with a weighted graph neural network. BioRxiv 2020\u201305","DOI":"10.1101\/2020.05.13.094953"},{"issue":"6","key":"9662_CR43","doi-asserted-by":"crossref","first-page":"342","DOI":"10.1093\/bioinformatics\/btad342","volume":"39","author":"J Lee","year":"2023","unstructured":"Lee J, Kim S, Hyun D, Lee N, Kim Y, Park C (2023) Deep single-cell rna-seq data clustering with graph prototypical contrastive learning. Bioinformatics 39(6):342","journal-title":"Bioinformatics"},{"issue":"10","key":"9662_CR44","doi-asserted-by":"crossref","first-page":"1867","DOI":"10.1101\/gr.271205.120","volume":"31","author":"N Alghamdi","year":"2021","unstructured":"Alghamdi N, Chang W, Dang P, Lu X, Wan C, Gampala S, Huang Z, Wang J, Ma Q, Zang Y et al (2021) A graph neural network model to estimate cell-wise metabolic flux using single-cell rna-seq data. Genome Res 31(10):1867\u20131884","journal-title":"Genome Res"},{"key":"9662_CR45","doi-asserted-by":"crossref","unstructured":"Lin E, Liu B, Lac L, Fung D, Leung C, Hu P: (2023) scgmm-vgae: a gaussian mixture model-based variational graph autoencoder algorithm for clustering single-cell rna-seq data. Mach Learn Sci Technol","DOI":"10.1088\/2632-2153\/acd7c3"},{"issue":"2","key":"9662_CR46","doi-asserted-by":"crossref","first-page":"506","DOI":"10.3390\/genes14020506","volume":"14","author":"R Bhadani","year":"2023","unstructured":"Bhadani R, Chen Z, An L (2023) Attention-based graph neural network for label propagation in single-cell omics. Genes 14(2):506","journal-title":"Genes"},{"issue":"3","key":"9662_CR47","doi-asserted-by":"crossref","first-page":"738","DOI":"10.1093\/bioinformatics\/btab700","volume":"38","author":"M Yuan","year":"2022","unstructured":"Yuan M, Chen L, Deng M (2022) scmra: a robust deep learning method to annotate scrna-seq data with multiple reference datasets. Bioinformatics 38(3):738\u2013745","journal-title":"Bioinformatics"},{"issue":"11","key":"9662_CR48","doi-asserted-by":"crossref","first-page":"2996","DOI":"10.1093\/bioinformatics\/btac199","volume":"38","author":"Q Yin","year":"2022","unstructured":"Yin Q, Liu Q, Fu Z, Zeng W, Zhang B, Zhang X, Jiang R, Lv H (2022) scgraph: a graph neural network-based approach to automatically identify cell types. Bioinformatics 38(11):2996\u20133003","journal-title":"Bioinformatics"},{"issue":"10","key":"9662_CR49","doi-asserted-by":"crossref","first-page":"1458","DOI":"10.1038\/s41587-022-01284-4","volume":"40","author":"Z-J Cao","year":"2022","unstructured":"Cao Z-J, Gao G (2022) Multi-omics single-cell data integration and regulatory inference with graph-linked embedding. Nature Biotechnol 40(10):1458\u20131466","journal-title":"Nature Biotechnol"},{"issue":"4","key":"9662_CR50","doi-asserted-by":"crossref","first-page":"389","DOI":"10.1038\/s42256-022-00469-5","volume":"4","author":"H Li","year":"2022","unstructured":"Li H, Sun Y, Hong H, Huang X, Tao H, Huang Q, Wang L, Xu K, Gan J, Chen H et al (2022) Inferring transcription factor regulatory networks from single-cell atac-seq data based on graph neural networks. Nature Mach Intell 4(4):389\u2013400","journal-title":"Nature Mach Intell"},{"issue":"1","key":"9662_CR51","doi-asserted-by":"crossref","first-page":"3826","DOI":"10.1038\/s41467-021-24172-y","volume":"12","author":"Q Song","year":"2021","unstructured":"Song Q, Su J, Zhang W (2021) scgcn is a graph convolutional networks algorithm for knowledge transfer in single cell omics. Nature Commun 12(1):3826","journal-title":"Nature Commun"},{"key":"9662_CR52","doi-asserted-by":"crossref","unstructured":"Liu Y, Zhang J, Wang S, Zhang W, Zeng X, Kwoh CK (2022) A heterogeneous graph cross-omics attention model for single-cell representation learning. In: 2022 IEEE international conference on bioinformatics and biomedicine (BIBM), pp 270\u2013275. IEEE","DOI":"10.1109\/BIBM55620.2022.9995680"},{"key":"9662_CR53","unstructured":"Wu AP-Y, Singh R, Walsh CA, Berger B (2023) An econometric lens resolves cell-state parallax. bioRxiv, 2023"},{"issue":"1","key":"9662_CR54","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13059-020-02214-w","volume":"21","author":"Y Yuan","year":"2020","unstructured":"Yuan Y, Bar-Joseph Z (2020) Gcng: graph convolutional networks for inferring gene interaction from spatial transcriptomics data. Genome Biol 21(1):1\u201316","journal-title":"Genome Biol"},{"key":"9662_CR55","doi-asserted-by":"crossref","unstructured":"Li Y, Luo Y (2023) Spatial transcriptomic cell-type deconvolution using graph neural networks. bioRxiv","DOI":"10.1101\/2023.03.10.532112"},{"key":"9662_CR56","unstructured":"Cang Z, Ning X, Nie A, Xu M, Zhang J (2021) Scan-it: domain segmentation of spatial transcriptomics images by graph neural network. In: BMVC: proceedings of the british machine vision conference. british machine vision conference. vol 32. NIH Public Access"},{"issue":"5","key":"9662_CR57","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1038\/s41576-018-0088-9","volume":"20","author":"VY Kiselev","year":"2019","unstructured":"Kiselev VY, Andrews TS, Hemberg M (2019) Challenges in unsupervised clustering of single-cell rna-seq data. Nature Rev Genet 20(5):273\u2013282","journal-title":"Nature Rev Genet"},{"issue":"8","key":"9662_CR58","doi-asserted-by":"crossref","first-page":"550","DOI":"10.1038\/s41576-023-00586-w","volume":"24","author":"L Heumos","year":"2023","unstructured":"Heumos L, Schaar AC, Lance C, Litinetskaya A, Drost F, Zappia L, L\u00fccken MD, Strobl DC, Henao J, Curion F et al (2023) Best practices for single-cell analysis across modalities. Nature Rev Genet 24(8):550","journal-title":"Nature Rev Genet"},{"issue":"11","key":"9662_CR59","first-page":"104","volume":"9","author":"L Maaten","year":"2008","unstructured":"Maaten L, Hinton G (2008) Visualizing data using t-sne. J Mach Learn Res 9(11):104","journal-title":"J Mach Learn Res"},{"key":"9662_CR60","doi-asserted-by":"crossref","unstructured":"McInnes L, Healy J, Melville J (2018) Umap: Uniform manifold approximation and projection for dimension reduction. arXiv preprint arXiv:1802.03426","DOI":"10.21105\/joss.00861"},{"issue":"12","key":"9662_CR61","doi-asserted-by":"crossref","first-page":"1053","DOI":"10.1038\/s41592-018-0229-2","volume":"15","author":"R Lopez","year":"2018","unstructured":"Lopez R, Regier J, Cole MB, Jordan MI, Yosef N (2018) Deep generative modeling for single-cell transcriptomics. Nature Methods 15(12):1053\u20131058","journal-title":"Nature Methods"},{"issue":"1","key":"9662_CR62","doi-asserted-by":"crossref","first-page":"284","DOI":"10.1038\/s41467-017-02554-5","volume":"9","author":"D Risso","year":"2018","unstructured":"Risso D, Perraudeau F, Gribkova S, Dudoit S, Vert J-P (2018) A general and flexible method for signal extraction from single-cell rna-seq data. Nature Commun 9(1):284","journal-title":"Nature Commun"},{"issue":"6","key":"9662_CR63","doi-asserted-by":"crossref","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, Bader GD (2021) Tutorial: guidelines for annotating single-cell transcriptomic maps using automated and manual methods. Nature Protoc 16(6):2749\u20132764","journal-title":"Nature Protoc"},{"issue":"1","key":"9662_CR64","doi-asserted-by":"crossref","first-page":"3493","DOI":"10.1038\/s41467-020-17217-1","volume":"11","author":"A Aalto","year":"2020","unstructured":"Aalto A, Viitasaari L, Ilmonen P, Mombaerts L, Gon\u00e7alves J (2020) Gene regulatory network inference from sparsely sampled noisy data. Nature Commun 11(1):3493","journal-title":"Nature Commun"},{"key":"9662_CR65","doi-asserted-by":"crossref","unstructured":"Badia-i-Mompel P, Wessels L, M\u00fcller-Dott S, Trimbour R, Ramirez\u00a0Flores RO, Argelaguet R, Saez-Rodriguez J (2023) Gene regulatory network inference in the era of single-cell multi-omics. Nature Rev Genet 1\u201316","DOI":"10.1038\/s41576-023-00618-5"},{"issue":"1","key":"9662_CR66","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13059-021-02568-9","volume":"23","author":"Z Liu","year":"2022","unstructured":"Liu Z, Sun D, Wang C (2022) Evaluation of cell-cell interaction methods by integrating single-cell rna sequencing data with spatial information. Genome Biol 23(1):1\u201338","journal-title":"Genome Biol"},{"issue":"8","key":"9662_CR67","doi-asserted-by":"crossref","first-page":"1211","DOI":"10.3390\/biom13081211","volume":"13","author":"Z Xie","year":"2023","unstructured":"Xie Z, Li X, Mora A (2023) A comparison of cell-cell interaction prediction tools based on scrna-seq data. Biomolecules 13(8):1211","journal-title":"Biomolecules"},{"issue":"2","key":"9662_CR68","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1038\/s41592-019-0690-6","volume":"17","author":"A Pratapa","year":"2020","unstructured":"Pratapa A, Jalihal AP, Law JN, Bharadwaj A, Murali T (2020) Benchmarking algorithms for gene regulatory network inference from single-cell transcriptomic data. Nature Methods 17(2):147\u2013154","journal-title":"Nature Methods"},{"key":"9662_CR69","doi-asserted-by":"crossref","first-page":"48994","DOI":"10.7554\/eLife.48994","volume":"8","author":"AJ Tarashansky","year":"2019","unstructured":"Tarashansky AJ, Xue Y, Li P, Quake SR, Wang B (2019) Self-assembling manifolds in single-cell rna sequencing data. Elife 8:48994","journal-title":"Elife"},{"issue":"1","key":"9662_CR70","doi-asserted-by":"crossref","first-page":"14049","DOI":"10.1038\/ncomms14049","volume":"8","author":"GX Zheng","year":"2017","unstructured":"Zheng GX, Terry JM, Belgrader P, Ryvkin P, Bent ZW, Wilson R, Ziraldo SB, Wheeler TD, McDermott GP, Zhu J et al (2017) Massively parallel digital transcriptional profiling of single cells. Nature Commun 8(1):14049","journal-title":"Nature Commun"},{"issue":"7","key":"9662_CR71","doi-asserted-by":"crossref","first-page":"978","DOI":"10.1038\/s41591-018-0045-3","volume":"24","author":"X Guo","year":"2018","unstructured":"Guo X, Zhang Y, Zheng L, Zheng C, Song J, Zhang Q, Kang B, Liu Z, Jin L, Xing R et al (2018) Global characterization of t cells in non-small-cell lung cancer by single-cell sequencing. Nature Med 24(7):978\u2013985","journal-title":"Nature Med"},{"issue":"5","key":"9662_CR72","doi-asserted-by":"crossref","first-page":"1187","DOI":"10.1016\/j.cell.2015.04.044","volume":"161","author":"AM Klein","year":"2015","unstructured":"Klein AM, Mazutis L, Akartuna I, Tallapragada N, Veres A, Li V, Peshkin L, Weitz DA, Kirschner MW (2015) Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells. Cell 161(5):1187\u20131201","journal-title":"Cell"},{"issue":"4","key":"9662_CR73","doi-asserted-by":"crossref","first-page":"346","DOI":"10.1016\/j.cels.2016.08.011","volume":"3","author":"M Baron","year":"2016","unstructured":"Baron M, Veres A, Wolock SL, Faust AL, Gaujoux R, Vetere A, Ryu JH, Wagner BK, Shen-Orr SS, Klein AM et al (2016) A single-cell transcriptomic map of the human and mouse pancreas reveals inter-and intra-cell population structure. Cell Syst 3(4):346\u2013360","journal-title":"Cell Syst"},{"issue":"7808","key":"9662_CR74","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1038\/s41586-020-2157-4","volume":"581","author":"X Han","year":"2020","unstructured":"Han X, Zhou Z, Fei L, Sun H, Wang R, Chen Y, Chen H, Wang J, Tang H, Ge W et al (2020) Construction of a human cell landscape at single-cell level. Nature 581(7808):303\u2013309","journal-title":"Nature"},{"issue":"5","key":"9662_CR75","doi-asserted-by":"crossref","first-page":"1091","DOI":"10.1016\/j.cell.2018.02.001","volume":"172","author":"X Han","year":"2018","unstructured":"Han X, Wang R, Zhou Y, Fei L, Sun H, Lai S, Saadatpour A, Zhou Z, Chen H, Ye F et al (2018) Mapping the mouse cell atlas by microwell-seq. Cell 172(5):1091\u20131107","journal-title":"Cell"},{"issue":"3","key":"9662_CR76","doi-asserted-by":"crossref","first-page":"425","DOI":"10.1038\/s41593-020-00787-0","volume":"24","author":"KR Maynard","year":"2021","unstructured":"Maynard KR, Collado-Torres L, Weber LM, Uytingco C, Barry BK, Williams SR, Catallini JL, Tran MN, Besich Z, Tippani M et al (2021) Transcriptome-scale spatial gene expression in the human dorsolateral prefrontal cortex. Nature Neurosci 24(3):425\u2013436","journal-title":"Nature Neurosci"},{"issue":"D1","key":"9662_CR77","doi-asserted-by":"crossref","first-page":"996","DOI":"10.1093\/nar\/gks1042","volume":"41","author":"SM Sunkin","year":"2012","unstructured":"Sunkin SM, Ng L, Lau C, Dolbeare T, Gilbert TL, Thompson CL, Hawrylycz M, Dang C (2012) Allen brain atlas: an integrated spatio-temporal portal for exploring the central nervous system. Nucleic Acids Res 41(D1):996\u20131008","journal-title":"Nucleic Acids Res"},{"issue":"3","key":"9662_CR78","doi-asserted-by":"crossref","first-page":"313","DOI":"10.1038\/s41587-020-0739-1","volume":"39","author":"RR Stickels","year":"2021","unstructured":"Stickels RR, Murray E, Kumar P, Li J, Marshall JL, Di Bella DJ, Arlotta P, Macosko EZ, Chen F (2021) Highly sensitive spatial transcriptomics at near-cellular resolution with slide-seqv2. Nature Biotechnol 39(3):313\u2013319","journal-title":"Nature Biotechnol"},{"issue":"12","key":"9662_CR79","doi-asserted-by":"crossref","first-page":"1794","DOI":"10.1038\/s41587-022-01483-z","volume":"40","author":"S He","year":"2022","unstructured":"He S, Bhatt R, Brown C, Brown EA, Buhr DL, Chantranuvatana K, Danaher P, Dunaway D, Garrison RG, Geiss G et al (2022) High-plex imaging of rna and proteins at subcellular resolution in fixed tissue by spatial molecular imaging. Nature Biotechnol 40(12):1794\u20131806","journal-title":"Nature Biotechnol"},{"issue":"6400","key":"9662_CR80","doi-asserted-by":"crossref","first-page":"5691","DOI":"10.1126\/science.aat5691","volume":"361","author":"X Wang","year":"2018","unstructured":"Wang X, Allen WE, Wright MA, Sylwestrak EL, Samusik N, Vesuna S, Evans K, Liu C, Ramakrishnan C, Liu J et al (2018) Three-dimensional intact-tissue sequencing of single-cell transcriptional states. Science 361(6400):5691","journal-title":"Science"},{"issue":"6416","key":"9662_CR81","doi-asserted-by":"crossref","first-page":"5324","DOI":"10.1126\/science.aau5324","volume":"362","author":"JR Moffitt","year":"2018","unstructured":"Moffitt JR, Bambah-Mukku D, Eichhorn SW, Vaughn E, Shekhar K, Perez JD, Rubinstein ND, Hao J, Regev A, Dulac C et al (2018) Molecular, spatial, and functional single-cell profiling of the hypothalamic preoptic region. Science 362(6416):5324","journal-title":"Science"},{"key":"9662_CR82","doi-asserted-by":"crossref","unstructured":"13 B.W.H..H.M.S.C.L...P.P.J..K.R., Medicine Creighton Chad J. 22 23 Donehower Lawrence A. 22 23 24\u00a025, G., Systems Biology Reynolds Sheila 31 Kreisberg Richard B. 31 Bernard Brady 31 Bressler Ryan 31 Erkkila Timo 32 Lin Jake 31 Thorsson Vesteinn 31 Zhang Wei 33 Shmulevich Ilya\u00a031, I., et\u00a0al.: Comprehensive molecular portraits of human breast tumours. Nature 490(7418), 61\u201370 (2012)","DOI":"10.1038\/nature11412"},{"issue":"7492","key":"9662_CR83","doi-asserted-by":"crossref","first-page":"315","DOI":"10.1038\/nature12965","volume":"507","author":"CGAR Network","year":"2014","unstructured":"Network CGAR et al (2014) Comprehensive molecular characterization of urothelial bladder carcinoma. Nature 507(7492):315","journal-title":"Nature"},{"key":"9662_CR84","unstructured":"Luecken MD, Burkhardt DB, Cannoodt R, Lance C, Agrawal A, Aliee H, Chen AT, Deconinck L, Detweiler AM, Granados AA et\u00a0al (2021) A sandbox for prediction and integration of dna, rna, and proteins in single cells. In: 35th conference on neural information processing systems (NeurIPS 2021) track on datasets and benchmarks"},{"key":"9662_CR85","doi-asserted-by":"crossref","first-page":"61973","DOI":"10.7554\/eLife.61973","volume":"10","author":"TB Buus","year":"2021","unstructured":"Buus TB, Herrera A, Ivanova E, Mimitou E, Cheng A, Herati RS, Papagiannakopoulos T, Smibert P, Odum N, Koralov SB (2021) Improving oligo-conjugated antibody signal in multimodal single-cell analysis. Elife 10:61973","journal-title":"Elife"},{"issue":"6518","key":"9662_CR86","doi-asserted-by":"crossref","first-page":"7721","DOI":"10.1126\/science.aba7721","volume":"370","author":"J Cao","year":"2020","unstructured":"Cao J, O\u2019Day DR, Pliner HA, Kingsley PD, Deng M, Daza RM, Zager MA, Aldinger KA, Blecher-Gonen R, Zhang F et al (2020) A human cell atlas of fetal gene expression. Science 370(6518):7721","journal-title":"Science"},{"key":"9662_CR87","doi-asserted-by":"crossref","first-page":"146","DOI":"10.1016\/j.inffus.2017.10.006","volume":"42","author":"Q Zhang","year":"2018","unstructured":"Zhang Q, Yang LT, Chen Z, Li P (2018) A survey on deep learning for big data. Information Fusion 42:146\u2013157","journal-title":"Information Fusion"},{"issue":"10","key":"9662_CR88","doi-asserted-by":"crossref","first-page":"627","DOI":"10.1038\/s41576-021-00370-8","volume":"22","author":"SK Longo","year":"2021","unstructured":"Longo SK, Guo MG, Ji AL, Khavari PA (2021) Integrating single-cell and spatial transcriptomics to elucidate intercellular tissue dynamics. Nature Rev Genet 22(10):627\u2013644","journal-title":"Nature Rev Genet"},{"key":"9662_CR89","unstructured":"Brody, S., Alon, U., Yahav, E.: How attentive are graph attention networks? arXiv preprint arXiv:2105.14491 (2021)"},{"key":"9662_CR90","unstructured":"Joshi-Tope, G., Gillespie, M., Vastrik, I., D\u2019Eustachio, P., Schmidt, E., Bono, B., Jassal, B., Gopinath, G., Wu, G., Matthews, L., et\u00a0al.: Reactome: a knowledgebase of biological pathways. Nucleic acids research 33(suppl_1), 428\u2013432 (2005)"},{"issue":"8","key":"9662_CR91","doi-asserted-by":"crossref","first-page":"1363","DOI":"10.1101\/gr.240663.118","volume":"29","author":"L Garcia-Alonso","year":"2019","unstructured":"Garcia-Alonso L, Holland CH, Ibrahim MM, Turei D, Saez-Rodriguez J (2019) Benchmark and integration of resources for the estimation of human transcription factor activities. Genome Res 29(8):1363\u20131375","journal-title":"Genome Res"},{"issue":"D1","key":"9662_CR92","doi-asserted-by":"crossref","first-page":"380","DOI":"10.1093\/nar\/gkx1013","volume":"46","author":"H Han","year":"2018","unstructured":"Han H, Cho J-W, Lee S, Yun A, Kim H, Bae D, Yang S, Kim CY, Lee M, Kim E et al (2018) Trrust v2: an expanded reference database of human and mouse transcriptional regulatory interactions. Nucleic Acids Res 46(D1):380\u2013386","journal-title":"Nucleic Acids Res"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-024-09662-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-024-09662-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-024-09662-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,5,27]],"date-time":"2024-05-27T08:20:01Z","timestamp":1716798001000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-024-09662-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4,17]]},"references-count":92,"journal-issue":{"issue":"17","published-print":{"date-parts":[[2024,6]]}},"alternative-id":["9662"],"URL":"https:\/\/doi.org\/10.1007\/s00521-024-09662-6","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,4,17]]},"assertion":[{"value":"23 August 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 March 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 April 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors have no conflict of interest to declare that are relevant to the content of this article.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}