{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,7]],"date-time":"2026-05-07T10:19:48Z","timestamp":1778149188950,"version":"3.51.4"},"reference-count":31,"publisher":"Oxford University Press (OUP)","issue":"5","license":[{"start":{"date-parts":[[2024,9,17]],"date-time":"2024-09-17T00:00:00Z","timestamp":1726531200000},"content-version":"vor","delay-in-days":54,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000026","name":"National Institute on Drug Abuse","doi-asserted-by":"publisher","award":["R01DA043501"],"award-info":[{"award-number":["R01DA043501"]}],"id":[{"id":"10.13039\/100000026","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,7,25]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>With rapidly evolving high-throughput technologies and consistently decreasing costs, collecting multimodal omics data in large-scale studies has become feasible. Although studying multiomics provides a new comprehensive approach in understanding the complex biological mechanisms of human diseases, the high dimensionality of omics data and the complexity of the interactions among various omics levels in contributing to disease phenotypes present tremendous analytical challenges. There is a great need of novel analytical methods to address these challenges and to facilitate multiomics analyses. In this paper, we propose a multimodal functional deep learning (MFDL) method for the analysis of high-dimensional multiomics data. The MFDL method models the complex relationships between multiomics variants and disease phenotypes through the hierarchical structure of deep neural networks and handles high-dimensional omics data using the functional data analysis technique. Furthermore, MFDL leverages the structure of the multimodal model to capture interactions between different types of omics data. Through simulation studies and real-data applications, we demonstrate the advantages of MFDL in terms of prediction accuracy and its robustness to the high dimensionality and noise within the data.<\/jats:p>","DOI":"10.1093\/bib\/bbae448","type":"journal-article","created":{"date-parts":[[2024,9,17]],"date-time":"2024-09-17T17:09:26Z","timestamp":1726592966000},"source":"Crossref","is-referenced-by-count":13,"title":["Multimodal functional deep learning for multiomics data"],"prefix":"10.1093","volume":"25","author":[{"given":"Yuan","family":"Zhou","sequence":"first","affiliation":[{"name":"Department of Biostatistics, University of Florida , 2004 Mowry Rd, Gainesville, FL 32611, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8760-7785","authenticated-orcid":false,"given":"Pei","family":"Geng","sequence":"additional","affiliation":[{"name":"Department of Mathematics and Statistics, University of New Hampshire , 33 Academic Way, Durham, NH 03824, USA"}]},{"given":"Shan","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Statistics and Probability, Michigan State University , 619 Red Cedar Road, East Lansing, MI 48824, USA"}]},{"given":"Feifei","family":"Xiao","sequence":"additional","affiliation":[{"name":"Department of Biostatistics, University of Florida , 2004 Mowry Rd, Gainesville, FL 32611, USA"}]},{"given":"Guoshuai","family":"Cai","sequence":"additional","affiliation":[{"name":"Department of Surgery, University of Florida , Gainesville, 1600 SW Archer Rd, FL 32611, USA"}]},{"given":"Li","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Biostatistics, University of Florida , 2004 Mowry Rd, Gainesville, FL 32611, USA"}]},{"name":"For the Alzheimer\u2019s Disease Neuroimaging 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