Du, LeiHuang, HengYan, JingwenKim, SungeunRisacher, ShannonInlow, MarkMoore, JasonSaykin, Andrew J.Shen, Li2016-09-192016-09-192016Du, L., Huang, H., Yan, J., Kim, S., Risacher, S., Inlow, M., … Shen, L. (2016). Structured sparse CCA for brain imaging genetics via graph OSCAR. BMC Systems Biology, 10(3), 335–345. http://doi.org/10.1186/s12918-016-0312-1https://hdl.handle.net/1805/10992Recently, 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.Attribution 3.0 United StatesBrain imaging geneticsCanonical correlation analysisMachine learningStructured sparse modelArticle