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Due to the fact that the number of patients in omics data is much smaller than the number of genes, multi-view spectral clustering based on similarity learning has been widely developed. However, these algorithms still suffer some problems, such as over-reliance on the quality of pre-defined similarity matrices for clustering results, inability to reasonably handle noise and redundant information in high-dimensional omics data, ignoring complementary information between omics data, etc. This paper proposes multi-view spectral clustering with latent representation learning (MSCLRL) method to alleviate the above problems. First, MSCLRL generates a corresponding low-dimensional latent representation for each omics data, which can effectively retain the unique information of each omics and improve the robustness and accuracy of the similarity matrix. Second, the obtained latent representations are assigned appropriate weights by MSCLRL, and global similarity learning is performed to generate an integrated similarity matrix. Third, the integrated similarity matrix is used to feed back and update the low-dimensional representation of each omics. Finally, the final integrated similarity matrix is used for clustering. In 10 benchmark multi-omics datasets and 2 separate cancer case studies, the experiments confirmed that the proposed method obtained statistically and biologically meaningful cancer subtypes.<\/jats:p>","DOI":"10.1093\/bib\/bbac500","type":"journal-article","created":{"date-parts":[[2022,11,29]],"date-time":"2022-11-29T15:36:31Z","timestamp":1669736191000},"source":"Crossref","is-referenced-by-count":22,"title":["Multi-view spectral clustering with latent representation learning for applications on multi-omics cancer subtyping"],"prefix":"10.1093","volume":"24","author":[{"given":"Shuguang","family":"Ge","sequence":"first","affiliation":[{"name":"School of Information and Control Engineering, China University of Mining and Technology , No. 1, Daxue Road, 221116 Xuzhou, Jiangsu , China"},{"name":"Engineering Research Center of Intelligent Control for Underground Space, Ministry of Education, China University of Mining and Technology , No. 1, Daxue Road, 221116 Xuzhou, Jiangsu , 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