{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Kim M"],"funding":["NSF","NIBIB NIH HHS","NIA NIH HHS","NIA","NIMH NIH HHS","National Research Foundation of Korea","National Institutes of Health","NIBIB","NLM","McDonnell Center for Systems Neuroscience","NIH Blueprint for Neuroscience Research","NLM NIH HHS","NIH HHS","National Science Foundation"],"pagination":["102297"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC8792314"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["76"],"pubmed_abstract":["The advances in technologies for acquiring brain imaging and high-throughput genetic data allow the researcher to access a large amount of multi-modal data. Although the sparse canonical correlation analysis is a powerful bi-multivariate association analysis technique for feature selection, we are still facing major challenges in integrating multi-modal imaging genetic data and yielding biologically meaningful interpretation of imaging genetic findings. In this study, we propose a novel multi-task learning based structured sparse canonical correlation analysis (MTS2CCA) to deliver interpretable results and improve integration in imaging genetics studies. We perform comparative studies with state-of-the-art competing methods on both simulation and real imaging genetic data. On the simulatio"],"journal":["Medical image analysis"],"pubmed_title":["Multi-task learning based structured sparse canonical correlation analysis for brain imaging genetics."],"pmcid":["PMC8792314"],"funding_grant_id":["S10 OD023495","RF1 AG063481","IIS 1837964","U01 AG068057","R01 LM013463","U54 MH091657","R01 EB022574","R01 AG058854","NRF-2020R1A6A3A03038525"],"pubmed_authors":["Saykin AJ","Shen L","Moore JH","Liu K","Yan J","Min EJ","Long Q","Kim M"],"additional_accession":[]},"is_claimable":false,"name":"Multi-task learning based structured sparse canonical correlation analysis for brain imaging genetics.","description":"The advances in technologies for acquiring brain imaging and high-throughput genetic data allow the researcher to access a large amount of multi-modal data. Although the sparse canonical correlation analysis is a powerful bi-multivariate association analysis technique for feature selection, we are still facing major challenges in integrating multi-modal imaging genetic data and yielding biologically meaningful interpretation of imaging genetic findings. In this study, we propose a novel multi-task learning based structured sparse canonical correlation analysis (MTS2CCA) to deliver interpretable results and improve integration in imaging genetics studies. We perform comparative studies with state-of-the-art competing methods on both simulation and real imaging genetic data. On the simulatio","dates":{"release":"2022-01-01T00:00:00Z","publication":"2022 Feb","modification":"2026-04-08T10:46:05.525Z","creation":"2025-04-21T15:51:45.081Z"},"accession":"S-EPMC8792314","cross_references":{"pubmed":["34871929"],"doi":["10.1016/j.media.2021.102297"]}}