{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,13]],"date-time":"2026-01-13T15:34:37Z","timestamp":1768318477665,"version":"3.49.0"},"reference-count":62,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2024,9,1]],"date-time":"2024-09-01T00:00:00Z","timestamp":1725148800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2024,9,1]],"date-time":"2024-09-01T00:00:00Z","timestamp":1725148800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2024,9,1]],"date-time":"2024-09-01T00:00:00Z","timestamp":1725148800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2024,9,1]],"date-time":"2024-09-01T00:00:00Z","timestamp":1725148800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2024,9,1]],"date-time":"2024-09-01T00:00:00Z","timestamp":1725148800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2024,9,1]],"date-time":"2024-09-01T00:00:00Z","timestamp":1725148800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,9,1]],"date-time":"2024-09-01T00:00:00Z","timestamp":1725148800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62172122"],"award-info":[{"award-number":["62172122"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["clinicalkey.fr","clinicalkey.jp","clinicalkey.com.au","clinicalkey.es","clinicalkey.com","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Computers in Biology and Medicine"],"published-print":{"date-parts":[[2024,9]]},"DOI":"10.1016\/j.compbiomed.2024.108921","type":"journal-article","created":{"date-parts":[[2024,7,26]],"date-time":"2024-07-26T09:08:20Z","timestamp":1721984900000},"page":"108921","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":2,"special_numbering":"C","title":["Single-cell RNA sequencing data analysis utilizing multi-type graph neural networks"],"prefix":"10.1016","volume":"179","author":[{"given":"Li","family":"Xu","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0009-6429-4451","authenticated-orcid":false,"given":"Zhenpeng","family":"Li","sequence":"additional","affiliation":[]},{"given":"Jiaxu","family":"Ren","sequence":"additional","affiliation":[]},{"given":"Shuaipeng","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Yiming","family":"Xu","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"issue":"3","key":"10.1016\/j.compbiomed.2024.108921_b1","doi-asserted-by":"crossref","first-page":"bbad152","DOI":"10.1093\/bib\/bbad152","article-title":"Dimensionality reduction and visualization of single-cell RNA-seq data with an improved deep variational autoencoder","volume":"24","author":"Jiang","year":"2023","journal-title":"Brief. Bioinform."},{"issue":"2","key":"10.1016\/j.compbiomed.2024.108921_b2","doi-asserted-by":"crossref","first-page":"204","DOI":"10.1038\/s41587-022-01452-6","article-title":"Mostly natural sequencing-by-synthesis for scRNA-seq using ultima sequencing","volume":"41","author":"Simmons","year":"2023","journal-title":"Nat. Biotechnol."},{"issue":"4","key":"10.1016\/j.compbiomed.2024.108921_b3","doi-asserted-by":"crossref","first-page":"1196","DOI":"10.1093\/bib\/bbz062","article-title":"Clustering and classification methods for single-cell RNA-sequencing data","volume":"21","author":"Qi","year":"2020","journal-title":"Brief. Bioinform."},{"issue":"1","key":"10.1016\/j.compbiomed.2024.108921_b4","article-title":"Graph embedding and Gaussian mixture variational autoencoder network for end-to-end analysis of single-cell RNA sequencing data","volume":"3","author":"Xu","year":"2023","journal-title":"Cell Rep. Methods"},{"key":"10.1016\/j.compbiomed.2024.108921_b5","first-page":"15710","article-title":"Effective clustering of scRNA-seq data to identify biomarkers without user input","volume":"vol. 35","author":"Chowdhury","year":"2021"},{"issue":"1","key":"10.1016\/j.compbiomed.2024.108921_b6","doi-asserted-by":"crossref","first-page":"400","DOI":"10.1038\/s41467-023-36134-7","article-title":"Topological identification and interpretation for single-cell gene regulation elucidation across multiple platforms using scMGCA","volume":"14","author":"Yu","year":"2023","journal-title":"Nature Commun."},{"issue":"1","key":"10.1016\/j.compbiomed.2024.108921_b7","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1038\/s42256-023-00763-w","article-title":"Reconstructing growth and dynamic trajectories from single-cell transcriptomics data","volume":"6","author":"Sha","year":"2024","journal-title":"Nat. Mach. Intell."},{"issue":"2","key":"10.1016\/j.compbiomed.2024.108921_b8","doi-asserted-by":"crossref","first-page":"bbab568","DOI":"10.1093\/bib\/bbab568","article-title":"A hybrid deep learning framework for gene regulatory network inference from single-cell transcriptomic data","volume":"23","author":"Zhao","year":"2022","journal-title":"Brief. Bioinform."},{"issue":"1","key":"10.1016\/j.compbiomed.2024.108921_b9","doi-asserted-by":"crossref","first-page":"3575","DOI":"10.1038\/s41467-024-47884-3","article-title":"scLENS: data-driven signal detection for unbiased scRNA-seq data analysis","volume":"15","author":"Kim","year":"2024","journal-title":"Nature Commun."},{"issue":"21","key":"10.1016\/j.compbiomed.2024.108921_b10","doi-asserted-by":"crossref","first-page":"3964","DOI":"10.1093\/bioinformatics\/btab420","article-title":"HGC: Fast hierarchical clustering for large-scale single-cell data","volume":"37","author":"Zou","year":"2021","journal-title":"Bioinformatics"},{"issue":"6","key":"10.1016\/j.compbiomed.2024.108921_b11","doi-asserted-by":"crossref","DOI":"10.1093\/bib\/bbab236","article-title":"Consensus clustering of single-cell RNA-seq data by enhancing network affinity","volume":"22","author":"Cui","year":"2021","journal-title":"Brief. Bioinform."},{"key":"10.1016\/j.compbiomed.2024.108921_b12","series-title":"Proceedings of the 12th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics","first-page":"1","article-title":"Fast and memory-efficient scRNA-seq k-means clustering with various distances","author":"Baker","year":"2021"},{"issue":"1","key":"10.1016\/j.compbiomed.2024.108921_b13","doi-asserted-by":"crossref","first-page":"1901","DOI":"10.1038\/s41467-022-29576-y","article-title":"A universal deep neural network for in-depth cleaning of single-cell RNA-seq data","volume":"13","author":"Li","year":"2022","journal-title":"Nature Commun."},{"issue":"1","key":"10.1016\/j.compbiomed.2024.108921_b14","doi-asserted-by":"crossref","first-page":"1873","DOI":"10.1038\/s41467-021-22008-3","article-title":"Model-based deep embedding for constrained clustering analysis of single cell RNA-seq data","volume":"12","author":"Tian","year":"2021","journal-title":"Nat. Commun."},{"key":"10.1016\/j.compbiomed.2024.108921_b15","first-page":"4671","article-title":"Zinb-based graph embedding autoencoder for single-cell RNA-SEQ interpretations","volume":"vol. 36","author":"Yu","year":"2022"},{"issue":"3","key":"10.1016\/j.compbiomed.2024.108921_b16","doi-asserted-by":"crossref","DOI":"10.1093\/bib\/bbad149","article-title":"SSNMDI: A novel joint learning model of semi-supervised non-negative matrix factorization and data imputation for clustering of single-cell RNA-seq data","volume":"24","author":"Qiu","year":"2023","journal-title":"Brief. Bioinform."},{"issue":"3","key":"10.1016\/j.compbiomed.2024.108921_b17","doi-asserted-by":"crossref","first-page":"716","DOI":"10.1016\/j.cell.2018.05.061","article-title":"Recovering gene interactions from single-cell data using data diffusion","volume":"174","author":"Van Dijk","year":"2018","journal-title":"Cell"},{"key":"10.1016\/j.compbiomed.2024.108921_b18","series-title":"International Forum on Financial Mathematics and Financial Technology","first-page":"203","article-title":"LLE based K-nearest neighbor smoothing for scRNA-seq data imputation","author":"Feng","year":"2021"},{"issue":"1","key":"10.1016\/j.compbiomed.2024.108921_b19","doi-asserted-by":"crossref","first-page":"566","DOI":"10.1109\/TCBB.2022.3161131","article-title":"Network-based structural learning nonnegative matrix factorization algorithm for clustering of scRNA-seq data","volume":"20","author":"Wu","year":"2022","journal-title":"IEEE\/ACM Trans. Comput. Biol. Bioinform."},{"issue":"1","key":"10.1016\/j.compbiomed.2024.108921_b20","doi-asserted-by":"crossref","first-page":"997","DOI":"10.1038\/s41467-018-03405-7","article-title":"An accurate and robust imputation method scimpute for single-cell RNA-seq data","volume":"9","author":"Li","year":"2018","journal-title":"Nat. Commun."},{"issue":"2","key":"10.1016\/j.compbiomed.2024.108921_b21","doi-asserted-by":"crossref","first-page":"466","DOI":"10.1073\/pnas.1817715116","article-title":"Semisoft clustering of single-cell data","volume":"116","author":"Zhu","year":"2019","journal-title":"Proc. Natl. Acad. Sci."},{"issue":"1","key":"10.1016\/j.compbiomed.2024.108921_b22","doi-asserted-by":"crossref","first-page":"bbab345","DOI":"10.1093\/bib\/bbab345","article-title":"A comparison of deep learning-based pre-processing and clustering approaches for single-cell RNA sequencing data","volume":"23","author":"Wang","year":"2022","journal-title":"Brief. Bioinform."},{"key":"10.1016\/j.compbiomed.2024.108921_b23","series-title":"International Conference on Machine Learning","first-page":"478","article-title":"Unsupervised deep embedding for clustering analysis","author":"Xie","year":"2016"},{"issue":"4","key":"10.1016\/j.compbiomed.2024.108921_b24","doi-asserted-by":"crossref","first-page":"191","DOI":"10.1038\/s42256-019-0037-0","article-title":"Clustering single-cell RNA-seq data with a model-based deep learning approach","volume":"1","author":"Tian","year":"2019","journal-title":"Nat. Mach. Intell."},{"issue":"5","key":"10.1016\/j.compbiomed.2024.108921_b25","doi-asserted-by":"crossref","DOI":"10.1093\/bioinformatics\/btae293","article-title":"scTPC: A novel semisupervised deep clustering model for scRNA-seq data","volume":"40","author":"Qiu","year":"2024","journal-title":"Bioinformatics"},{"issue":"2","key":"10.1016\/j.compbiomed.2024.108921_b26","doi-asserted-by":"crossref","DOI":"10.1093\/bib\/bbae068","article-title":"scAMAC: Self-supervised clustering of scRNA-seq data based on adaptive multi-scale autoencoder","volume":"25","author":"Tan","year":"2024","journal-title":"Brief. Bioinform."},{"issue":"5","key":"10.1016\/j.compbiomed.2024.108921_b27","doi-asserted-by":"crossref","first-page":"bbab090","DOI":"10.1093\/bib\/bbab090","article-title":"Deep embedded clustering with multiple objectives on scRNA-seq data","volume":"22","author":"Li","year":"2021","journal-title":"Brief. Bioinform."},{"issue":"1","key":"10.1016\/j.compbiomed.2024.108921_b28","doi-asserted-by":"crossref","DOI":"10.1093\/bioinformatics\/btae020","article-title":"scMAE: A masked autoencoder for single-cell RNA-seq clustering","volume":"40","author":"Fang","year":"2024","journal-title":"Bioinformatics"},{"issue":"5","key":"10.1016\/j.compbiomed.2024.108921_b29","doi-asserted-by":"crossref","first-page":"bbac172","DOI":"10.1093\/bib\/bbac172","article-title":"A parameter-free deep embedded clustering method for single-cell RNA-seq data","volume":"23","author":"Zeng","year":"2022","journal-title":"Brief. Bioinform."},{"issue":"1","key":"10.1016\/j.compbiomed.2024.108921_b30","doi-asserted-by":"crossref","DOI":"10.1093\/bib\/bbab321","article-title":"ScCAEs: Deep clustering of single-cell RNA-seq via convolutional autoencoder embedding and soft K-means","volume":"23","author":"Hu","year":"2022","journal-title":"Brief. Bioinform."},{"issue":"1","key":"10.1016\/j.compbiomed.2024.108921_b31","doi-asserted-by":"crossref","first-page":"264","DOI":"10.1186\/s12864-024-10160-1","article-title":"scFSNN: A feature selection method based on neural network for single-cell RNA-seq data","volume":"25","author":"Peng","year":"2024","journal-title":"BMC Genom."},{"issue":"5","key":"10.1016\/j.compbiomed.2024.108921_b32","doi-asserted-by":"crossref","DOI":"10.1093\/bib\/bbab105","article-title":"Critical downstream analysis steps for single-cell RNA sequencing data","volume":"22","author":"Zhang","year":"2021","journal-title":"Brief. Bioinform."},{"key":"10.1016\/j.compbiomed.2024.108921_b33","series-title":"2020 IEEE International Conference on Bioinformatics and Biomedicine","first-page":"519","article-title":"Accurately clustering single-cell RNA-seq data by capturing structural relations between cells through graph convolutional network","author":"Zeng","year":"2020"},{"issue":"2","key":"10.1016\/j.compbiomed.2024.108921_b34","doi-asserted-by":"crossref","DOI":"10.1093\/bib\/bbac018","article-title":"Deep structural clustering for single-cell RNA-seq data jointly through autoencoder and graph neural network","volume":"23","author":"Gan","year":"2022","journal-title":"Brief. Bioinform."},{"issue":"1","key":"10.1016\/j.compbiomed.2024.108921_b35","doi-asserted-by":"crossref","DOI":"10.1093\/bib\/bbad469","article-title":"GTAD: A graph-based approach for cell spatial composition inference from integrated scRNA-seq and ST-seq data","volume":"25","author":"Zhang","year":"2024","journal-title":"Brief. Bioinform."},{"issue":"1","key":"10.1016\/j.compbiomed.2024.108921_b36","doi-asserted-by":"crossref","first-page":"1029","DOI":"10.1038\/s41467-021-21312-2","article-title":"Fast and precise single-cell data analysis using a hierarchical autoencoder","volume":"12","author":"Tran","year":"2021","journal-title":"Nat. Commun."},{"issue":"1","key":"10.1016\/j.compbiomed.2024.108921_b37","doi-asserted-by":"crossref","first-page":"1882","DOI":"10.1038\/s41467-021-22197-x","article-title":"scGNN is a novel graph neural network framework for single-cell RNA-seq analyses","volume":"12","author":"Wang","year":"2021","journal-title":"Nat. Commun."},{"key":"10.1016\/j.compbiomed.2024.108921_b38","first-page":"5036","article-title":"Unsupervised deep embedded fusion representation of single-cell transcriptomics","volume":"vol. 37","author":"Cheng","year":"2023"},{"issue":"2","key":"10.1016\/j.compbiomed.2024.108921_b39","doi-asserted-by":"crossref","DOI":"10.1093\/bioinformatics\/btae091","article-title":"scSemiGCN: Boosting cell-type annotation from noise-resistant graph neural networks with extremely limited supervision","volume":"40","author":"Yang","year":"2024","journal-title":"Bioinformatics"},{"issue":"2","key":"10.1016\/j.compbiomed.2024.108921_b40","doi-asserted-by":"crossref","first-page":"bbae018","DOI":"10.1093\/bib\/bbae018","article-title":"scHybridBERT: Integrating gene regulation and cell graph for spatiotemporal dynamics in single-cell clustering","volume":"25","author":"Wei","year":"2024","journal-title":"Brief. Bioinform."},{"issue":"1","key":"10.1016\/j.compbiomed.2024.108921_b41","doi-asserted-by":"crossref","DOI":"10.1093\/bib\/bbad481","article-title":"Advancing single-cell RNA-seq data analysis through the fusion of multi-layer perceptron and graph neural network","volume":"25","author":"Feng","year":"2024","journal-title":"Brief. Bioinform."},{"key":"10.1016\/j.compbiomed.2024.108921_b42","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1016\/j.ymeth.2023.02.008","article-title":"Single-cell RNA-seq data analysis based on directed graph neural network","volume":"211","author":"Feng","year":"2023","journal-title":"Methods"},{"issue":"3","key":"10.1016\/j.compbiomed.2024.108921_b43","doi-asserted-by":"crossref","first-page":"btad098","DOI":"10.1093\/bioinformatics\/btad098","article-title":"scGCL: An imputation method for scRNA-seq data based on graph contrastive learning","volume":"39","author":"Xiong","year":"2023","journal-title":"Bioinformatics"},{"issue":"4","key":"10.1016\/j.compbiomed.2024.108921_b44","doi-asserted-by":"crossref","first-page":"1037","DOI":"10.1093\/bioinformatics\/btab787","article-title":"GNN-based embedding for clustering scRNA-seq data","volume":"38","author":"Ciortan","year":"2022","journal-title":"Bioinformatics"},{"key":"10.1016\/j.compbiomed.2024.108921_b45","series-title":"International Symposium on Bioinformatics Research and Applications","first-page":"534","article-title":"ScDA: A denoising AutoEncoder based dimensionality reduction for single-cell RNA-seq data","author":"Zhu","year":"2021"},{"issue":"5","key":"10.1016\/j.compbiomed.2024.108921_b46","doi-asserted-by":"crossref","first-page":"btae293","DOI":"10.1093\/bioinformatics\/btae293","article-title":"scTPC: A novel semisupervised deep clustering model for scRNA-seq data","volume":"40","author":"Qiu","year":"2024","journal-title":"Bioinformatics"},{"issue":"1","key":"10.1016\/j.compbiomed.2024.108921_b47","doi-asserted-by":"crossref","first-page":"284","DOI":"10.1038\/s41467-017-02554-5","article-title":"A general and flexible method for signal extraction from single-cell RNA-seq data","volume":"9","author":"Risso","year":"2018","journal-title":"Nat. Commun."},{"issue":"5","key":"10.1016\/j.compbiomed.2024.108921_b48","doi-asserted-by":"crossref","first-page":"1277","DOI":"10.1093\/bioinformatics\/btab804","article-title":"CellVGAE: An unsupervised scRNA-seq analysis workflow with graph attention networks","volume":"38","author":"Buterez","year":"2022","journal-title":"Bioinformatics"},{"issue":"5","key":"10.1016\/j.compbiomed.2024.108921_b49","doi-asserted-by":"crossref","DOI":"10.1016\/j.isci.2021.102393","article-title":"Imputing single-cell RNA-seq data by combining graph convolution and autoencoder neural networks","volume":"24","author":"Rao","year":"2021","journal-title":"Iscience"},{"key":"10.1016\/j.compbiomed.2024.108921_b50","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiolchem.2023.107924","article-title":"Dual-GCN-based deep clustering with triplet contrast for scRNA-seq data analysis","volume":"106","author":"Wang","year":"2023","journal-title":"Comput. Biol. Chem."},{"issue":"1","key":"10.1016\/j.compbiomed.2024.108921_b51","doi-asserted-by":"crossref","first-page":"536","DOI":"10.1186\/s12859-022-05085-z","article-title":"SC3s: efficient scaling of single cell consensus clustering to millions of cells","volume":"23","author":"Quah","year":"2022","journal-title":"BMC Bioinform."},{"issue":"2","key":"10.1016\/j.compbiomed.2024.108921_b52","doi-asserted-by":"crossref","first-page":"308","DOI":"10.1109\/TCSS.2020.2964197","article-title":"C-blondel: An efficient louvain-based dynamic community detection algorithm","volume":"7","author":"Seifikar","year":"2020","journal-title":"IEEE Trans. Comput. Soc. Syst."},{"issue":"2","key":"10.1016\/j.compbiomed.2024.108921_b53","doi-asserted-by":"crossref","DOI":"10.1093\/bib\/bbad026","article-title":"SMURF: Embedding single-cell RNA-seq data with matrix factorization preserving self-consistency","volume":"24","author":"Pu","year":"2023","journal-title":"Brief. Bioinform."},{"issue":"5","key":"10.1016\/j.compbiomed.2024.108921_b54","doi-asserted-by":"crossref","first-page":"1187","DOI":"10.1016\/j.cell.2015.04.044","article-title":"Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells","volume":"161","author":"Klein","year":"2015","journal-title":"Cell"},{"issue":"6352","key":"10.1016\/j.compbiomed.2024.108921_b55","doi-asserted-by":"crossref","first-page":"661","DOI":"10.1126\/science.aam8940","article-title":"Comprehensive single-cell transcriptional profiling of a multicellular organism","volume":"357","author":"Cao","year":"2017","journal-title":"Science"},{"issue":"6226","key":"10.1016\/j.compbiomed.2024.108921_b56","doi-asserted-by":"crossref","first-page":"1138","DOI":"10.1126\/science.aaa1934","article-title":"Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq","volume":"347","author":"Zeisel","year":"2015","journal-title":"Science"},{"issue":"1","key":"10.1016\/j.compbiomed.2024.108921_b57","doi-asserted-by":"crossref","first-page":"14049","DOI":"10.1038\/ncomms14049","article-title":"Massively parallel digital transcriptional profiling of single cells","volume":"8","author":"Zheng","year":"2017","journal-title":"Nat. Commun."},{"issue":"4","key":"10.1016\/j.compbiomed.2024.108921_b58","doi-asserted-by":"crossref","first-page":"346","DOI":"10.1016\/j.cels.2016.08.011","article-title":"A single-cell transcriptomic map of the human and mouse pancreas reveals inter-and intra-cell population structure","volume":"3","author":"Baron","year":"2016","journal-title":"Cell Syst."},{"issue":"6402","key":"10.1016\/j.compbiomed.2024.108921_b59","doi-asserted-by":"crossref","first-page":"594","DOI":"10.1126\/science.aat1699","article-title":"Single-cell transcriptomes from human kidneys reveal the cellular identity of renal tumors","volume":"361","author":"Young","year":"2018","journal-title":"Science"},{"issue":"3","key":"10.1016\/j.compbiomed.2024.108921_b60","doi-asserted-by":"crossref","first-page":"0000","DOI":"10.1093\/bib\/bbad124","article-title":"Accurate and interpretable gene expression imputation on scRNA-seq data using IGSimpute","volume":"24","author":"Xu","year":"2023","journal-title":"Brief. Bioinform."},{"issue":"9","key":"10.1016\/j.compbiomed.2024.108921_b61","doi-asserted-by":"crossref","first-page":"2778","DOI":"10.1093\/bioinformatics\/btaa042","article-title":"PARC: Ultrafast and accurate clustering of phenotypic data of millions of single cells","volume":"36","author":"Stassen","year":"2020","journal-title":"Bioinformatics"},{"issue":"6","key":"10.1016\/j.compbiomed.2024.108921_b62","doi-asserted-by":"crossref","first-page":"3685","DOI":"10.1109\/TCBB.2021.3126641","article-title":"scCDG: A method based on DAE and GCN for scRNA-seq data analysis","volume":"19","author":"Wang","year":"2022","journal-title":"IEEE\/ACM Trans. Comput. Biol. Bioinform."}],"container-title":["Computers in Biology and Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0010482524010060?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0010482524010060?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,1,22]],"date-time":"2025-01-22T16:19:24Z","timestamp":1737562764000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0010482524010060"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9]]},"references-count":62,"alternative-id":["S0010482524010060"],"URL":"https:\/\/doi.org\/10.1016\/j.compbiomed.2024.108921","relation":{},"ISSN":["0010-4825"],"issn-type":[{"value":"0010-4825","type":"print"}],"subject":[],"published":{"date-parts":[[2024,9]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Single-cell RNA sequencing data analysis utilizing multi-type graph neural networks","name":"articletitle","label":"Article Title"},{"value":"Computers in Biology and Medicine","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.compbiomed.2024.108921","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2024 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"108921"}}