Sparse representation based biomarker selection for schizophrenia with integrated analysis of fMRI and SNPs.

ABSTRACT: Integrative analysis of multiple data types can take advantage of their complementary information and therefore may provide higher power to identify potential biomarkers that would be missed using individual data analysis. Due to different natures of diverse data modality, data integration is challenging. Here we address the data integration problem by developing a generalized sparse model (GSM) using weighting factors to integrate multi-modality data for biomarker selection. As an example, we applied the GSM model to a joint analysis of two types of schizophrenia data sets: 759,075 SNPs and 153,594 functional magnetic resonance imaging (fMRI) voxels in 208 subjects (92 cases/116 controls). To solve this small-sample-large-variable problem, we developed a novel sparse representation based

SUBMITTER: Cao H 

PROVIDER: S-EPMC4130811 | biostudies-literature | 2014 Nov

REPOSITORIES: biostudies-literature