Multi-task learning based structured sparse canonical correlation analysis for brain imaging genetics.

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

SUBMITTER: Kim M 

PROVIDER: S-EPMC8792314 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature