{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,23]],"date-time":"2025-10-23T21:02:39Z","timestamp":1761253359639,"version":"3.37.3"},"reference-count":28,"publisher":"Oxford University Press (OUP)","issue":"20","license":[{"start":{"date-parts":[[2019,3,27]],"date-time":"2019-03-27T00:00:00Z","timestamp":1553644800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key R&D Program of China","doi-asserted-by":"crossref","award":["2018YFC0910500"],"award-info":[{"award-number":["2018YFC0910500"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["11471082"],"award-info":[{"award-number":["11471082"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003399","name":"Science and Technology Commission of Shanghai Municipality","doi-asserted-by":"publisher","award":["16JC1402600","2018SHZDZX01"],"award-info":[{"award-number":["16JC1402600","2018SHZDZX01"]}],"id":[{"id":"10.13039\/501100003399","id-type":"DOI","asserted-by":"publisher"}]},{"name":"ZHANGJIANG LAB"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019,10,15]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Multiview clustering has attracted much attention in recent years. Several models and algorithms have been proposed for finding the clusters. However, these methods are developed either to find the consistent\/common clusters across different views, or to identify the differential clusters among different views. In reality, both consistent and differential clusters may exist in multiview datasets. Thus, development of simultaneous clustering methods such that both the consistent and the differential clusters can be identified is of great importance.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>In this paper, we proposed one method for simultaneous clustering of multiview data based on manifold optimization. The binary optimization model for finding the clusters is relaxed to a real value optimization problem on the Stiefel manifold, which is solved by the line-search algorithm on manifold. We applied the proposed method to both simulation data and four real datasets from TCGA. Both studies show that when the underlying clusters are consistent, our method performs competitive to the state-of-the-art algorithms. When there are differential clusters, our method performs much better. In the real data study, we performed experiments on cancer stratification and differential cluster (module) identification across multiple cancer subtypes. For the patients of different subtypes, both consistent clusters and differential clusters are identified at the same time. The proposed method identifies more clusters that are enriched by gene ontology and KEGG pathways. The differential clusters could be used to explain the different mechanisms for the cancer development in the patients of different subtypes.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>Codes can be downloaded from: http:\/\/homepage.fudan.edu.cn\/sqzhang\/files\/2018\/12\/MVCMOcode.zip.<\/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\/btz217","type":"journal-article","created":{"date-parts":[[2019,3,26]],"date-time":"2019-03-26T15:13:43Z","timestamp":1553613223000},"page":"4029-4037","source":"Crossref","is-referenced-by-count":15,"title":["Simultaneous clustering of multiview biomedical data using manifold optimization"],"prefix":"10.1093","volume":"35","author":[{"given":"Yun","family":"Yu","sequence":"first","affiliation":[{"name":"School of Mathematical Sciences, Fudan University , Shanghai, China"}]},{"given":"Lei-Hong","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Mathematical Sciences, Soochow University , Suzhou, China"}]},{"given":"Shuqin","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Mathematical Sciences, Fudan University , Shanghai, China"},{"name":"Center for Computational Systems Biology, Fudan University , Shanghai, China"},{"name":"Shanghai Key Laboratory for Contemporary Applied Mathematics, Fudan University , Shanghai, China"},{"name":"Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence of Ministry of Education, Fudan University , Shanghai, China"}]}],"member":"286","published-online":{"date-parts":[[2019,3,27]]},"reference":[{"key":"2023013108284217700_btz217-B1","doi-asserted-by":"crossref","DOI":"10.1515\/9781400830244","volume-title":"Optimization Algorithms on Matrix Manifolds","author":"Absil","year":"2008"},{"key":"2023013108284217700_btz217-B2","doi-asserted-by":"crossref","first-page":"6745","DOI":"10.1073\/pnas.96.12.6745","article-title":"Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays","volume":"96","author":"Alon","year":"1999","journal-title":"Proc. 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