{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,30]],"date-time":"2026-03-30T13:51:09Z","timestamp":1774878669741,"version":"3.50.1"},"reference-count":46,"publisher":"Oxford University Press (OUP)","issue":"12","license":[{"start":{"date-parts":[[2016,10,2]],"date-time":"2016-10-02T00:00:00Z","timestamp":1475366400000},"content-version":"vor","delay-in-days":1576,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/3.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2012,6,15]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Motivation: Recent advances in brain imaging and high-throughput genotyping techniques enable new approaches to study the influence of genetic and anatomical variations on brain functions and disorders. Traditional association studies typically perform independent and pairwise analysis among neuroimaging measures, cognitive scores and disease status, and ignore the important underlying interacting relationships between these units.<\/jats:p><jats:p>Results: To overcome this limitation, in this article, we propose a new sparse multimodal multitask learning method to reveal complex relationships from gene to brain to symptom. Our main contributions are three-fold: (i) introducing combined structured sparsity regularizations into multimodal multitask learning to integrate multidimensional heterogeneous imaging genetics data and identify multimodal biomarkers; (ii) utilizing a joint classification and regression learning model to identify disease-sensitive and cognition-relevant biomarkers; (iii) deriving a new efficient optimization algorithm to solve our non-smooth objective function and providing rigorous theoretical analysis on the global optimum convergency. Using the imaging genetics data from the Alzheimer's Disease Neuroimaging Initiative database, the effectiveness of the proposed method is demonstrated by clearly improved performance on predicting both cognitive scores and disease status. The identified multimodal biomarkers could predict not only disease status but also cognitive function to help elucidate the biological pathway from gene to brain structure and function, and to cognition and disease.<\/jats:p><jats:p>Availability: Software is publicly available at: http:\/\/ranger.uta.edu\/%7eheng\/multimodal\/<\/jats:p><jats:p>Contact: \u00a0heng@uta.edu; shenli@iupui.edu<\/jats:p>","DOI":"10.1093\/bioinformatics\/bts228","type":"journal-article","created":{"date-parts":[[2012,6,11]],"date-time":"2012-06-11T14:09:18Z","timestamp":1339423758000},"page":"i127-i136","source":"Crossref","is-referenced-by-count":116,"title":["Identifying disease sensitive and quantitative trait-relevant biomarkers from multidimensional heterogeneous imaging genetics data via sparse multimodal multitask learning"],"prefix":"10.1093","volume":"28","author":[{"given":"Hua","family":"Wang","sequence":"first","affiliation":[{"name":"1 Department of Computer Science and Engineering, University of Texas at Arlington, TX 76019 and 2Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN 46202, USA"}]},{"given":"Feiping","family":"Nie","sequence":"additional","affiliation":[{"name":"1 Department of Computer Science and Engineering, University of Texas at Arlington, TX 76019 and 2Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN 46202, USA"}]},{"given":"Heng","family":"Huang","sequence":"additional","affiliation":[{"name":"1 Department of Computer Science and Engineering, University of Texas at Arlington, TX 76019 and 2Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN 46202, USA"}]},{"given":"Shannon L.","family":"Risacher","sequence":"additional","affiliation":[{"name":"1 Department of Computer Science and Engineering, University of Texas at Arlington, TX 76019 and 2Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN 46202, USA"}]},{"given":"Andrew J.","family":"Saykin","sequence":"additional","affiliation":[{"name":"1 Department of Computer Science and Engineering, University of Texas at Arlington, TX 76019 and 2Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN 46202, USA"}]},{"given":"Li","family":"Shen","sequence":"additional","affiliation":[{"name":"1 Department of Computer Science and Engineering, University of Texas at Arlington, TX 76019 and 2Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN 46202, USA"}]},{"name":"For the Alzheimer's Disease Neuroimaging Initiative","sequence":"additional","affiliation":[]}],"member":"286","published-online":{"date-parts":[[2012,6,9]]},"reference":[{"key":"2023012512333665600_B1","first-page":"360","article-title":"Bootstrapping","author":"Abney","year":"2002","journal-title":"Annual Meeting of the Association for Computational Linguistics"},{"key":"2023012512333665600_B2","first-page":"41","article-title":"Multi-task feature learning","volume-title":"Advances in Neural Information Processing System (NIPS)","author":"Argyriou","year":"2007"},{"key":"2023012512333665600_B3","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1007\/s10994-007-5040-8","article-title":"Convex multitask feature learning","volume":"73","author":"Argyriou","year":"2008","journal-title":"Machine Learning"},{"key":"2023012512333665600_B4","doi-asserted-by":"crossref","first-page":"805","DOI":"10.1006\/nimg.2000.0582","article-title":"Voxel-based morphometry\u2013the methods","volume":"11","author":"Ashburner","year":"2000","journal-title":"Neuroimage"},{"key":"2023012512333665600_B5","first-page":"6","article-title":"Multiple Kernel Learning, Conic Duality, and the SMOAlgorithm","volume-title":"International Conference on Machine Learning (ICML)","author":"Bach","year":"2004"},{"key":"2023012512333665600_B6","first-page":"423","article-title":"A general and unifying framework for feature construction, in image-based pattern classification","volume":"21","author":"Batmanghelich","year":"2009","journal-title":"Inf Process Med Imaging"},{"key":"2023012512333665600_B7","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1137\/080716542","article-title":"A fast iterative shrinkage-thresholding algorithm for linear inverse problems","volume":"2","author":"Beck","year":"2009","journal-title":"SIAM J. 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