{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,22]],"date-time":"2026-07-22T20:26:55Z","timestamp":1784752015219,"version":"3.55.0"},"reference-count":25,"publisher":"Oxford University Press (OUP)","issue":"2","license":[{"start":{"date-parts":[[2023,1,20]],"date-time":"2023-01-20T00:00:00Z","timestamp":1674172800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61972399"],"award-info":[{"award-number":["61972399"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,2,3]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>In single-cell transcriptomics applications, effective identification of cell types in multicellular organisms and in-depth study of the relationships between genes has become one of the main goals of bioinformatics research. However, data heterogeneity and random noise pose significant difficulties for scRNA-seq data analysis.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>We have proposed an adversarial dense graph convolutional network architecture for single-cell classification. Specifically, to enhance the representation of higher-order features and the organic combination between features, dense connectivity mechanism and attention-based feature aggregation are introduced for feature learning in convolutional neural networks. To preserve the features of the original data, we use a feature reconstruction module to assist the goal of single-cell classification. In addition, HNNVAT uses virtual adversarial training to improve the generalization and robustness. Experimental results show that our model outperforms the existing classical methods in terms of classification accuracy on benchmark datasets.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>The source code of HNNVAT is available at https:\/\/github.com\/DisscLab\/HNNVAT.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Supplementary information<\/jats:title>\n                  <jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btad043","type":"journal-article","created":{"date-parts":[[2023,1,19]],"date-time":"2023-01-19T14:13:51Z","timestamp":1674137631000},"source":"Crossref","is-referenced-by-count":12,"title":["Adversarial dense graph convolutional networks for single-cell classification"],"prefix":"10.1093","volume":"39","author":[{"given":"Kangwei","family":"Wang","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, China University of Mining and Technology , Xuzhou 221116, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1644-1006","authenticated-orcid":false,"given":"Zhengwei","family":"Li","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, China University of Mining and Technology , Xuzhou 221116, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhu-Hong","family":"You","sequence":"additional","affiliation":[{"name":"School of Computer Science, Northwestern Polytechnical University , Xi\u2019an 710072, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pengyong","family":"Han","sequence":"additional","affiliation":[{"name":"Central Lab, Changzhi Medical College , Changzhi 046000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ru","family":"Nie","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, China University of Mining and Technology , Xuzhou 221116, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2023,1,20]]},"reference":[{"key":"2023021110513813800_btad043-B1","doi-asserted-by":"crossref","first-page":"264","DOI":"10.1186\/s13059-019-1862-5","article-title":"scPred: accurate supervised method for cell-type classification from single-cell RNA-seq data","volume":"20","author":"Alquicira-Hernandez","year":"2019","journal-title":"Genome Biol"},{"key":"2023021110513813800_btad043-B2","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1038\/s41590-018-0276-y","article-title":"Reference-based analysis of lung single-cell sequencing reveals a transitional profibrotic macrophage","volume":"20","author":"Aran","year":"2019","journal-title":"Nat. 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