{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,4]],"date-time":"2026-04-04T17:41:53Z","timestamp":1775324513425,"version":"3.50.1"},"reference-count":16,"publisher":"Oxford University Press (OUP)","issue":"10","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2016,5,15]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Motivation: Structured sparse canonical correlation analysis (SCCA) models have been used to identify imaging genetic associations. These models either use group lasso or graph-guided fused lasso to conduct feature selection and feature grouping simultaneously. The group lasso based methods require prior knowledge to define the groups, which limits the capability when prior knowledge is incomplete or unavailable. The graph-guided methods overcome this drawback by using the sample correlation to define the constraint. However, they are sensitive to the sign of the sample correlation, which could introduce undesirable bias if the sign is wrongly estimated.<\/jats:p>\n               <jats:p>Results: We introduce a novel SCCA model with a new penalty, and develop an efficient optimization algorithm. Our method has a strong upper bound for the grouping effect for both positively and negatively correlated features. We show that our method performs better than or equally to three competing SCCA models on both synthetic and real data. In particular, our method identifies stronger canonical correlations and better canonical loading patterns, showing its promise for revealing interesting imaging genetic associations.<\/jats:p>\n               <jats:p>Availability and implementation: The Matlab code and sample data are freely available at http:\/\/www.iu.edu\/\u223cshenlab\/tools\/angscca\/.<\/jats:p>\n               <jats:p>Contact: \u00a0shenli@iu.edu<\/jats:p>\n               <jats:p>Supplementary information: \u00a0Supplementary data are available at Bioinformatics online.<\/jats:p>","DOI":"10.1093\/bioinformatics\/btw033","type":"journal-article","created":{"date-parts":[[2016,2,15]],"date-time":"2016-02-15T01:09:07Z","timestamp":1455498547000},"page":"1544-1551","source":"Crossref","is-referenced-by-count":103,"title":["Structured sparse canonical correlation analysis for brain imaging genetics: an improved GraphNet method"],"prefix":"10.1093","volume":"32","author":[{"given":"Lei","family":"Du","sequence":"first","affiliation":[{"name":"1 Department of Radiology and Imaging Sciences, Indiana University, Indianapolis, IN, USA,"}]},{"given":"Heng","family":"Huang","sequence":"additional","affiliation":[{"name":"2 Department of Computer Science & Engineering, The University of Texas at Arlington, Arlington, TX, USA,"}]},{"given":"Jingwen","family":"Yan","sequence":"additional","affiliation":[{"name":"1 Department of Radiology and Imaging Sciences, Indiana University, Indianapolis, IN, USA,"}]},{"given":"Sungeun","family":"Kim","sequence":"additional","affiliation":[{"name":"1 Department of Radiology and Imaging Sciences, Indiana University, Indianapolis, IN, USA,"}]},{"given":"Shannon L.","family":"Risacher","sequence":"additional","affiliation":[{"name":"1 Department of Radiology and Imaging Sciences, Indiana University, Indianapolis, IN, USA,"}]},{"given":"Mark","family":"Inlow","sequence":"additional","affiliation":[{"name":"3 Department of Mathematics, Rose-Hulman Institute of Technology, Terre Haute, IN, USA and"}]},{"given":"Jason H.","family":"Moore","sequence":"additional","affiliation":[{"name":"4 Institute for Biomedical Informatics, School of Medicine, University of Pennsylvania, Philadelphia, PA, USA"}]},{"given":"Andrew J.","family":"Saykin","sequence":"additional","affiliation":[{"name":"1 Department of Radiology and Imaging Sciences, Indiana University, Indianapolis, IN, USA,"}]},{"given":"Li","family":"Shen","sequence":"additional","affiliation":[{"name":"1 Department of Radiology and Imaging Sciences, Indiana University, Indianapolis, IN, USA,"}]},{"name":"for the Alzheimer\u2019s Disease Neuroimaging Initiative","sequence":"additional","affiliation":[]}],"member":"286","published-online":{"date-parts":[[2016,1,21]]},"reference":[{"key":"2023020112272043400_btw033-B1","doi-asserted-by":"crossref","first-page":"244","DOI":"10.1093\/biostatistics\/kxs038","article-title":"Structure-constrained sparse canonical correlation analysis with an application to microbiome data analysis","volume":"14","author":"Chen","year":"2013","journal-title":"Biostatistics"},{"key":"2023020112272043400_btw033-B2","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1007\/s12561-011-9048-z","article-title":"An efficient optimization algorithm for structured sparse cca, with applications to eqtl mapping","volume":"4","author":"Chen","year":"2012","journal-title":"Stat. 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