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However, the analysis of scHi-C data is challenged by a large number of missing values. Here, we present a scHi-C data imputation model HiC-SGL, based on Subgraph extraction and graph representation learning. HiC-SGL can also learn informative low-dimensional embeddings of cells. We demonstrate that our method surpasses existing methods in terms of imputation accuracy and clustering performance by various metrics.<\/jats:p>","DOI":"10.1093\/bib\/bbad379","type":"journal-article","created":{"date-parts":[[2023,12,2]],"date-time":"2023-12-02T02:38:16Z","timestamp":1701484696000},"source":"Crossref","is-referenced-by-count":12,"title":["Subgraph extraction and graph representation learning for single cell Hi-C imputation and clustering"],"prefix":"10.1093","volume":"25","author":[{"given":"Jiahao","family":"Zheng","sequence":"first","affiliation":[{"name":"School of Computer Science and Engineering, Sun Yat-Sen University , 510006 Guangzhou , China"}]},{"given":"Yuedong","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Sun Yat-Sen University , 510006 Guangzhou , China"}]},{"given":"Zhiming","family":"Dai","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Sun 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