{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,31]],"date-time":"2025-12-31T05:37:09Z","timestamp":1767159429724,"version":"3.48.0"},"publisher-location":"New York, NY, USA","reference-count":15,"publisher":"ACM","funder":[{"name":"Korea National Institute of Health (KNIH) research project","award":["2024-ER-0801-01"],"award-info":[{"award-number":["2024-ER-0801-01"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2025,5,16]]},"DOI":"10.1145\/3761712.3761736","type":"proceedings-article","created":{"date-parts":[[2025,12,31]],"date-time":"2025-12-31T05:34:31Z","timestamp":1767159271000},"page":"84-88","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Cell-specific network-based cell type prediction via graph convolutional network using transcriptomics profiles"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2090-3330","authenticated-orcid":false,"given":"Joung Min","family":"Choi","sequence":"first","affiliation":[{"name":"Department of Computer Science, Virginia Tech, Blacksburg, Virginia, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0960-5829","authenticated-orcid":false,"given":"Heejoon","family":"Chae","sequence":"additional","affiliation":[{"name":"Division of Computer Science, Sookmyung Women's University, Seoul, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2025,12,30]]},"reference":[{"key":"e_1_3_3_1_2_2","unstructured":"2024. NanoString Human frontal cortex cosmx Dataset. https:\/\/nanostring.com\/products\/cosmx-spatial-molecular-imager\/ffpe-dataset\/human-frontal-cortex-ffpe-dataset\/"},{"key":"e_1_3_3_1_3_2","doi-asserted-by":"crossref","unstructured":"Erick Armingol Hratch\u00a0M Baghdassarian and Nathan\u00a0E Lewis. 2024. The diversification of methods for studying cell\u2013cell interactions and communication. Nature Reviews Genetics 25 6 (2024) 381\u2013400.","DOI":"10.1038\/s41576-023-00685-8"},{"key":"e_1_3_3_1_4_2","doi-asserted-by":"crossref","unstructured":"Joungmin Choi Je-Keun Rhee and Heejoon Chae. 2021. Cell subtype classification via representation learning based on a denoising autoencoder for single-cell RNA sequencing. IEEE Access 9 (2021) 14540\u201314548.","DOI":"10.1109\/ACCESS.2021.3052923"},{"key":"e_1_3_3_1_5_2","doi-asserted-by":"crossref","unstructured":"Hao Dai Lin Li Tao Zeng and Luonan Chen. 2019. Cell-specific network constructed by single-cell RNA sequencing data. Nucleic acids research 47 11 (2019) e62\u2013e62.","DOI":"10.1093\/nar\/gkz172"},{"key":"e_1_3_3_1_6_2","doi-asserted-by":"crossref","unstructured":"Christopher\u00a0Daniel Green Qianyi Ma Gabriel\u00a0L Manske Adrienne\u00a0Niederriter Shami Xianing Zheng Simone Marini Lindsay Moritz Caleb Sultan Stephen\u00a0J Gurczynski Bethany\u00a0B Moore et\u00a0al. 2018. A comprehensive roadmap of murine spermatogenesis defined by single-cell RNA-seq. Developmental cell 46 5 (2018) 651\u2013667.","DOI":"10.1016\/j.devcel.2018.07.025"},{"key":"e_1_3_3_1_7_2","doi-asserted-by":"crossref","unstructured":"Aleksandr Ianevski Anil\u00a0K Giri and Tero Aittokallio. 2022. Fully-automated and ultra-fast cell-type identification using specific marker combinations from single-cell transcriptomic data. Nature communications 13 1 (2022) 1246.","DOI":"10.1038\/s41467-022-28803-w"},{"key":"e_1_3_3_1_8_2","doi-asserted-by":"crossref","unstructured":"Amanda Janesick Robert Shelansky Andrew\u00a0D Gottscho et\u00a0al. 2023. High resolution mapping of the tumor microenvironment using integrated single-cell spatial and in situ analysis. Nature Communications 14 1 (2023) 8353.","DOI":"10.1038\/s41467-023-43458-x"},{"key":"e_1_3_3_1_9_2","doi-asserted-by":"crossref","unstructured":"Dragomirka Jovic Xue Liang Hua Zeng Lin Lin Fengping Xu and Yonglun Luo. 2022. Single-cell RNA sequencing technologies and applications: A brief overview. Clinical and translational medicine 12 3 (2022) e694.","DOI":"10.1002\/ctm2.694"},{"key":"e_1_3_3_1_10_2","unstructured":"Diederik\u00a0P Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/1412.6980 (2014)."},{"key":"e_1_3_3_1_11_2","volume-title":"International Conference on Learning Representations","author":"Kipf Thomas\u00a0N.","year":"2017","unstructured":"Thomas\u00a0N. Kipf and Max Welling. 2017. Semi-Supervised Classification with Graph Convolutional Networks. In International Conference on Learning Representations."},{"key":"e_1_3_3_1_12_2","doi-asserted-by":"crossref","unstructured":"Vivien Marx. 2021. Method of the Year: spatially resolved transcriptomics. Nature methods 18 1 (2021) 9\u201314.","DOI":"10.1038\/s41592-020-01033-y"},{"key":"e_1_3_3_1_13_2","doi-asserted-by":"crossref","unstructured":"Kristen Nader Misra Tasci Aleksandr Ianevski Andrew Erickson Emmy\u00a0W Verschuren Tero Aittokallio and Mitro Miihkinen. 2024. ScType enables fast and accurate cell type identification from spatial transcriptomics data. Bioinformatics 40 7 (2024) btae426.","DOI":"10.1093\/bioinformatics\/btae426"},{"key":"e_1_3_3_1_14_2","doi-asserted-by":"crossref","unstructured":"Rongbo Shen Lin Liu Zihan Wu et\u00a0al. 2022. Spatial-ID: a cell typing method for spatially resolved transcriptomics via transfer learning and spatial embedding. Nature communications 13 1 (2022) 7640.","DOI":"10.1038\/s41467-022-35288-0"},{"key":"e_1_3_3_1_15_2","doi-asserted-by":"crossref","unstructured":"Xuran Wang David Choi and Kathryn Roeder. 2021. Constructing local cell-specific networks from single-cell data. Proceedings of the National Academy of Sciences 118 51 (2021) e2113178118.","DOI":"10.1073\/pnas.2113178118"},{"key":"e_1_3_3_1_16_2","doi-asserted-by":"crossref","unstructured":"F\u00a0Alexander Wolf Philipp Angerer and Fabian\u00a0J Theis. 2018. SCANPY: large-scale single-cell gene expression data analysis. Genome biology 19 (2018) 1\u20135.","DOI":"10.1186\/s13059-017-1382-0"}],"event":{"name":"ICMHI 2025: 2025 9th International Conference on Medical and Health Informatics","location":"Kyoto Japan","acronym":"ICMHI 2025"},"container-title":["Proceedings of the 2025 9th International Conference on Medical and Health Informatics"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3761712.3761736","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,31]],"date-time":"2025-12-31T05:34:36Z","timestamp":1767159276000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3761712.3761736"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5,16]]},"references-count":15,"alternative-id":["10.1145\/3761712.3761736","10.1145\/3761712"],"URL":"https:\/\/doi.org\/10.1145\/3761712.3761736","relation":{},"subject":[],"published":{"date-parts":[[2025,5,16]]},"assertion":[{"value":"2025-12-30","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}