Joint Sparse Collaborative Regression on Imaging Genetics Study of Schizophrenia.

ABSTRACT: The imaging genetics approach generates large amount of high dimensional and multi-modal data, providing complementary information for comprehensive study of Schizophrenia, a complex mental disease. However, at the same time, the variety of these data in structures, resolutions, and formats makes their integrative study a forbidding task. In this paper, we propose a novel model called Joint Sparse Collaborative Regression (JSCoReg), which can extract class-specific features from different health conditions/disease classes. We first evaluate the performance of feature selection in terms of Receiver operating characteristic curve and the area under the ROC curve in the simulation experiment. We demonstrate that the JSCoReg model can achieve higher accuracy compared with similar models includ

SUBMITTER: Song X 

PROVIDER: S-EPMC10321021 | biostudies-literature | 2023 Mar-Apr

REPOSITORIES: biostudies-literature