{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Zhang Y"],"funding":["China Scholarship Council","Fundamental Research Funds for the Central Universities","NIBIB NIH HHS","Chang'an University","NIMH NIH HHS","Natural Science Foundation of Shaanxi Province","National Institutes of Health","NIAMS NIH HHS","NIGMS NIH HHS","National Science Foundation"],"pagination":["2621-2629"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC9675402"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["24(9)"],"pubmed_abstract":["Current developments of neuroimaging and genetics promote an integrative and compressive study of schizophrenia. However, it is still difficult to explore how gene mutations are related to brain abnormalities due to the high dimension but low sample size of these data. Conventional approaches reduce the dimension of dataset separately and then calculate the correlation, but ignore the effects of the response variables and the structure of data. To improve the identification of risk genes and abnormal brain regions on schizophrenia, in this paper, we propose a novel method called Independence and Structural sparsity Canonical Correlation Analysis (ISCCA). ISCCA combines independent component analysis (ICA) and Canonical Correlation Analysis (CCA) to reduce the collinear effects, which also "],"journal":["IEEE journal of biomedical and health informatics"],"pubmed_title":["Canonical Correlation Analysis of Imaging Genetics Data Based on Statistical Independence and Structural Sparsity."],"pmcid":["PMC9675402"],"funding_grant_id":["R01 AR059781","R01EB005846","R56 MH124925","300102329102","R01MH104680","201806565009","2019JM-536","R01 EB005846","R01 MH107354","R01 MH104680","R01EB006841","P20 GM103472","R01 EB006841","R01MH107354","P20GM103472","R01GM109068","R01 GM109068","RO1AR059781"],"pubmed_authors":["Li G","Wang YP","Zhang Y","Ju Y","Peng P","Calhoun VD"],"additional_accession":[]},"is_claimable":false,"name":"Canonical Correlation Analysis of Imaging Genetics Data Based on Statistical Independence and Structural Sparsity.","description":"Current developments of neuroimaging and genetics promote an integrative and compressive study of schizophrenia. However, it is still difficult to explore how gene mutations are related to brain abnormalities due to the high dimension but low sample size of these data. Conventional approaches reduce the dimension of dataset separately and then calculate the correlation, but ignore the effects of the response variables and the structure of data. To improve the identification of risk genes and abnormal brain regions on schizophrenia, in this paper, we propose a novel method called Independence and Structural sparsity Canonical Correlation Analysis (ISCCA). ISCCA combines independent component analysis (ICA) and Canonical Correlation Analysis (CCA) to reduce the collinear effects, which also ","dates":{"release":"2020-01-01T00:00:00Z","publication":"2020 Sep","modification":"2025-04-05T10:57:30.194Z","creation":"2025-04-05T10:57:30.194Z"},"accession":"S-EPMC9675402","cross_references":{"pubmed":["32071012"],"doi":["10.1109/JBHI.2020.2972581","10.1109/jbhi.2020.2972581"]}}