- Browse by Author
Browsing by Author "Moore, Jason"
Now showing 1 - 2 of 2
Results Per Page
Sort Options
Item Network-Based Genome Wide Study of Hippocampal Imaging Phenotype In Alzheimer's Disease To Identify Functional Interaction Modules(IEEE, 2017) Yao, Xiaohui; Yan, Jingwen; Risacher, Shannon; Moore, Jason; Saykin, Andrew; Shen, Li; Radiology and Imaging Sciences, School of MedicineIdentification of functional modules from biological network is a promising approach to enhance the statistical power of genome-wide association study (GWAS) and improve biological interpretation for complex diseases. The precise functions of genes are highly relevant to tissue context, while a majority of module identification studies are based on tissue-free biological networks that lacks phenotypic specificity. In this study, we propose a module identification method that maps the GWAS results of an imaging phenotype onto the corresponding tissue-specific functional interaction network by applying a machine learning framework. Ridge regression and support vector machine (SVM) models are constructed to re-prioritize GWAS results, followed by exploring hippocampus-relevant modules based on top predictions using GWAS top findings. We also propose a GWAS top-neighbor-based module identification approach and compare it with Ridge and SVM based approaches. Modules conserving both tissue specificity and GWAS discoveries are identified, showing the promise of the proposal method for providing insight into the mechanism of complex diseases.Item Structured sparse CCA for brain imaging genetics via graph OSCAR(Biomed Central, 2016) Du, Lei; Huang, Heng; Yan, Jingwen; Kim, Sungeun; Risacher, Shannon; Inlow, Mark; Moore, Jason; Saykin, Andrew J.; Shen, Li; Department of Radiology and Imaging Sciences, IU School of MedicineRecently, structured sparse canonical correlation analysis (SCCA) has received increased attention in brain imaging genetics studies. It can identify bi-multivariate imaging genetic associations as well as select relevant features with desired structure information. These SCCA methods either use the fused lasso regularizer to induce the smoothness between ordered features, or use the signed pairwise difference which is dependent on the estimated sign of sample correlation. Besides, several other structured SCCA models use the group lasso or graph fused lasso to encourage group structure, but they require the structure/group information provided in advance which sometimes is not available.