{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T15:52:07Z","timestamp":1791474727725,"version":"4.3.4"},"reference-count":39,"publisher":"Institute of Mathematical Statistics","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Ann. Appl. Stat."],"published-print":{"date-parts":[[2021,3,1]]},"abstract":"The biomarker networks measured by different modalities of data (e.g., structural magnetic resonance imaging (sMRI), diffusion tensor imaging (DTI)) may share the same true underlying biological model. In this work we propose a nodewise biomarker graphical model to leverage the shared mechanism between multimodality data to provide a more reliable estimation of the target modality network and account for the heterogeneity in networks due to differences between subjects and networks of external modality. Latent variables are introduced to represent the shared unobserved biological network, and the information from the external modality is incorporated to model the distribution of the underlying biological network. We propose an efficient approximation to the posterior expectation of the latent variables that reduces computational cost by at least 50%. The performance of the proposed method is demonstrated by extensive simulation studies and an application to construct gray matter brain atrophy network of Huntington\u2019s disease by using sMRI data and DTI data. The identified network connections are more consistent with clinical literature and better improve prediction in follow-up clinical outcomes and separate subjects into clinically meaningful subgroups with different prognosis than alternative methods.<\/jats:p>","DOI":"10.1214\/20-aoas1382","type":"journal-article","created":{"date-parts":[[2021,3,19]],"date-time":"2021-03-19T14:41:23Z","timestamp":1616164883000},"page":"64-87","source":"Crossref","is-referenced-by-count":3,"title":["Integrative network learning for multimodality biomarker data"],"prefix":"10.1214","volume":"15","author":[{"given":"Shanghong","family":"Xie","sequence":"first","affiliation":[{"name":"Department of Biostatistics, Mailman School of Public Health, Columbia University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Donglin","family":"Zeng","sequence":"additional","affiliation":[{"name":"Department of Biostatistics, Gillings School of Global Public Health, University of North Carolina at Chapel Hill"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuanjia","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Biostatistics, Mailman School of Public Health, Columbia University"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"108","reference":[{"key":"11","unstructured":"Foygel, R.<\/small> and Drton, M.<\/small> (2010). 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Supplement to \u201cIntegrative network learning for multimodality biomarker data.\u201d https:\/\/doi.org\/10.1214\/20-AOAS1382SUPPA<\/a>, https:\/\/doi.org\/10.1214\/20-AOAS1382SUPPB<\/a>","DOI":"10.1214\/20-AOAS1382SUPPB"}],"container-title":["The Annals of Applied Statistics"],"original-title":[],"deposited":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T15:02:50Z","timestamp":1791471770000},"score":1,"resource":{"primary":{"URL":"https:\/\/projecteuclid.org\/journals\/annals-of-applied-statistics\/volume-15\/issue-1\/Integrative-network-learning-for-multimodality-biomarker-data\/10.1214\/20-AOAS1382.full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,3,1]]},"references-count":39,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2021,3,1]]}},"URL":"https:\/\/doi.org\/10.1214\/20-aoas1382","relation":{},"ISSN":["1932-6157","1941-7330"],"issn-type":[{"value":"1932-6157","type":"print"},{"value":"1941-7330","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,3,1]]}}}