{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,28]],"date-time":"2026-03-28T11:46:05Z","timestamp":1774698365927,"version":"3.50.1"},"publisher-location":"Singapore","reference-count":21,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819756919","type":"print"},{"value":"9789819756926","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"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":[[2024]]},"DOI":"10.1007\/978-981-97-5692-6_2","type":"book-chapter","created":{"date-parts":[[2024,7,30]],"date-time":"2024-07-30T08:02:35Z","timestamp":1722326555000},"page":"15-24","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Adaptive Weight Sampling and Graph Transformer Neural Network Framework for Cell Type Annotation of Scrna-seq Data"],"prefix":"10.1007","author":[{"given":"Lin","family":"Yuan","sequence":"first","affiliation":[]},{"given":"Shengguo","family":"Sun","sequence":"additional","affiliation":[]},{"given":"Zhujun","family":"Li","sequence":"additional","affiliation":[]},{"given":"Shoukang","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Xingang","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Yushui","family":"Geng","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,7,31]]},"reference":[{"key":"2_CR1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pcbi.1011344","volume":"19","author":"L Yuan","year":"2023","unstructured":"Yuan, L., Zhao, J., Shen, Z., et al.: iCircDA-NEAE: accelerated attribute network embedding and dynamic convolutional autoencoder for circRNA-disease associations prediction. PLoS Comput. Biol. 19, e1011344 (2023)","journal-title":"PLoS Comput. Biol."},{"key":"2_CR2","doi-asserted-by":"publisher","first-page":"782","DOI":"10.1109\/TCBB.2018.2866836","volume":"16","author":"L Yuan","year":"2018","unstructured":"Yuan, L., Guo, L.-H., Yuan, C.-A., et al.: Integration of multi-omics data for gene regulatory network inference and application to breast cancer. IEEE\/ACM Trans. Comput. Biol. Bioinf. 16, 782\u2013791 (2018)","journal-title":"IEEE\/ACM Trans. Comput. Biol. Bioinf."},{"key":"2_CR3","doi-asserted-by":"publisher","first-page":"581","DOI":"10.1186\/s12864-022-08820-1","volume":"23","author":"Z Shen","year":"2022","unstructured":"Shen, Z., Shao, Y.L., Liu, W., et al.: Prediction of back-splicing sites for CircRNA formation based on convolutional neural networks. BMC Genomics 23, 581 (2022)","journal-title":"BMC Genomics"},{"key":"2_CR4","doi-asserted-by":"crossref","unstructured":"Leng, F., Mei, S., Zhou, X., et al.: DVsc: an automated framework for efficiently detecting viral infection from single-cell transcriptomics data. Genomics Proteomics Bioinform. qzad007 (2023)","DOI":"10.1093\/gpbjnl\/qzad007"},{"key":"2_CR5","doi-asserted-by":"publisher","first-page":"1154","DOI":"10.1109\/TCBB.2016.2609420","volume":"14","author":"L Yuan","year":"2016","unstructured":"Yuan, L., Zhu, L., Guo, W.-L., et al.: Nonconvex penalty based low-rank representation and sparse regression for eQTL mapping. IEEE\/ACM Trans. Comput. Biol. Bioinf. 14, 1154\u20131164 (2016)","journal-title":"IEEE\/ACM Trans. Comput. Biol. Bioinf."},{"key":"2_CR6","doi-asserted-by":"publisher","first-page":"332","DOI":"10.1186\/s12859-021-04256-8","volume":"22","author":"L Yuan","year":"2021","unstructured":"Yuan, L., Zhao, J., Sun, T., et al.: A machine learning framework that integrates multi-omics data predicts cancer-related LncRNAs. BMC Bioinformatics 22, 332 (2021)","journal-title":"BMC Bioinformatics"},{"key":"2_CR7","doi-asserted-by":"publisher","first-page":"e122","DOI":"10.1093\/nar\/gkab775","volume":"49","author":"X Shao","year":"2021","unstructured":"Shao, X., Yang, H., Zhuang, 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. 49, e122\u2013e122 (2021)","journal-title":"Nucleic Acids Res."},{"key":"2_CR8","doi-asserted-by":"publisher","first-page":"2996","DOI":"10.1093\/bioinformatics\/btac199","volume":"38","author":"Q Yin","year":"2022","unstructured":"Yin, Q., Liu, Q., Fu, Z., et al.: ScGraph: a graph neural network-based approach to automatically identify cell types. Bioinformatics 38, 2996\u20133003 (2022)","journal-title":"Bioinformatics"},{"key":"2_CR9","doi-asserted-by":"publisher","first-page":"506","DOI":"10.3390\/genes14020506","volume":"14","author":"R Bhadani","year":"2023","unstructured":"Bhadani, R., Chen, Z., An, L.: Attention-based graph neural network for label propagation in single-cell omics. Genes 14, 506 (2023)","journal-title":"Genes"},{"key":"2_CR10","doi-asserted-by":"publisher","first-page":"268","DOI":"10.1038\/s41586-018-0694-x","volume":"564","author":"L Zhang","year":"2018","unstructured":"Zhang, L., Yu, X., Zheng, L., et al.: Lineage tracking reveals dynamic relationships of T cells in colorectal cancer. Nature 564, 268\u2013272 (2018)","journal-title":"Nature"},{"key":"2_CR11","doi-asserted-by":"publisher","first-page":"998","DOI":"10.1016\/j.cell.2018.10.038","volume":"175","author":"M Sade-Feldman","year":"2018","unstructured":"Sade-Feldman, M., Yizhak, K., Bjorgaard, S.L., et al.: Defining T cell states associated with response to checkpoint immunotherapy in melanoma. Cell 175, 998\u20131013 (2018)","journal-title":"Cell"},{"key":"2_CR12","doi-asserted-by":"publisher","first-page":"442","DOI":"10.1016\/j.cell.2020.03.048","volume":"181","author":"L Zhang","year":"2020","unstructured":"Zhang, L., Li, Z., Skrzypczynska, K.M., et al.: Single-cell analyses inform mechanisms of myeloid-targeted therapies in colon cancer. Cell 181, 442\u2013459 (2020)","journal-title":"Cell"},{"key":"2_CR13","doi-asserted-by":"publisher","first-page":"346","DOI":"10.1016\/j.cels.2016.08.011","volume":"3","author":"M Baron","year":"2016","unstructured":"Baron, M., Veres, A., Wolock, S.L., et al.: A single-cell transcriptomic map of the human and mouse pancreas reveals inter- and intra-cell population structure. Cell Syst. 3, 346\u2013360 (2016)","journal-title":"Cell Syst."},{"key":"2_CR14","doi-asserted-by":"publisher","first-page":"608","DOI":"10.1016\/j.cmet.2016.08.018","volume":"24","author":"Y Xin","year":"2016","unstructured":"Xin, Y., Kim, J., Okamoto, H., et al.: RNA sequencing of single human islet cells reveals type 2 diabetes genes. Cell Metab. 24, 608\u2013615 (2016)","journal-title":"Cell Metab."},{"key":"2_CR15","doi-asserted-by":"publisher","first-page":"2285","DOI":"10.1038\/s41467-020-16164-1","volume":"11","author":"N Kim","year":"2020","unstructured":"Kim, N., Kim, H.K., Lee, K., et al.: Single-cell RNA sequencing demonstrates the molecular and cellular reprogramming of metastatic lung adenocarcinoma. Nat. Commun. 11, 2285 (2020)","journal-title":"Nat. Commun."},{"key":"2_CR16","doi-asserted-by":"publisher","first-page":"1153","DOI":"10.1038\/s41591-019-0468-5","volume":"25","author":"FA Vieira Braga","year":"2019","unstructured":"Vieira Braga, F.A., Kar, G., Berg, M., et al.: A cellular census of human lungs identifies novel cell states in health and in asthma. Nat. Med. 25, 1153\u20131163 (2019)","journal-title":"Nat. Med."},{"issue":"7727","key":"2_CR17","doi-asserted-by":"publisher","first-page":"367","DOI":"10.1038\/s41586-018-0590-4","volume":"562","author":"RV Stanley","year":"2018","unstructured":"Stanley, R.V., Webber, G.M., Zanini, J.T., et al.: Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris. Nature 562(7727), 367\u2013372 (2018). https:\/\/doi.org\/10.1038\/s41586-018-0590-4","journal-title":"Nature"},{"key":"2_CR18","doi-asserted-by":"publisher","first-page":"D607","DOI":"10.1093\/nar\/gky1131","volume":"47","author":"D Szklarczyk","year":"2019","unstructured":"Szklarczyk, D., Gable, A.L., Lyon, D., et al.: STRING v11: protein\u2013protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res. 47, D607\u2013D613 (2019)","journal-title":"Nucleic Acids Res."},{"key":"2_CR19","doi-asserted-by":"publisher","first-page":"545","DOI":"10.1038\/s42003-023-04928-6","volume":"6","author":"Y Cheng","year":"2023","unstructured":"Cheng, Y., Fan, X., Zhang, J., et al.: A scalable sparse neural network framework for rare cell type annotation of single-cell transcriptome data. Commun. Biol. 6, 545 (2023)","journal-title":"Commun. Biol."},{"key":"2_CR20","unstructured":"Wu, Q., Zhao, W., Yang, C., et al.: Simplifying and empowering transformers for large-graph representations. Adv. Neural Inf. Process. Syst. 36 (2024)"},{"key":"2_CR21","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1109\/TNN.2008.2005605","volume":"20","author":"F Scarselli","year":"2008","unstructured":"Scarselli, F., Gori, M., Tsoi, A.C., et al.: The graph neural network model. IEEE Trans. Neural Netw. 20, 61\u201380 (2008)","journal-title":"IEEE Trans. Neural Netw."}],"container-title":["Lecture Notes in Computer Science","Advanced Intelligent Computing in Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-97-5692-6_2","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,30]],"date-time":"2024-07-30T08:08:57Z","timestamp":1722326937000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-97-5692-6_2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9789819756919","9789819756926"],"references-count":21,"URL":"https:\/\/doi.org\/10.1007\/978-981-97-5692-6_2","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"31 July 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICIC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Intelligent Computing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Tianjin","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 August 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 August 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icic2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.ic-icc.cn\/2024\/index.htm","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}