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A multilayer multimodal detection and prediction model based on explainable artificial intelligence for Alzheimer’s disease

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dc.contributor.authorEl-Sappagh, Shaker
dc.contributor.authorAlonso, Jose M.
dc.contributor.authorIslam, S. M.Riazul
dc.contributor.authorSultan, Ahmad M.
dc.contributor.authorKwak, Kyung Sup
dc.contributor.institutionUniversity of Aberdeen.Computing Scienceen
dc.date.accessioned2023-09-26T08:17:01Z
dc.date.available2023-09-26T08:17:01Z
dc.date.issued2021-01-29
dc.descriptionFunding Information: The authors would like to thank Farid Badria, a professor of pharmacognosy and head of the Liver Research Lab, Mansoura University, Egypt, and Hosam Zaghloul, a professor in the Clinical Pathology Department, Faculty of Medicine, Mansoura University, Egypt, for their efforts to assist this work. for their assistance as medical experts to finish the experimental part of this study. This work was supported by National Research Foundation of Korea-Grant funded by the Korean Government (Ministry of Science and ICT)-NRF-2020R1A2B5B02002478). In addition, Dr. Jose M. Alonso is Ramon y Cajal Researcher (RYC-2016-19802), and its research is supported by the Spanish Ministry of Science, Innovation and Universities (grants RTI2018-099646-B-I00, TIN2017-84796-C2-1-R, TIN2017-90773-REDT, and RED2018-102641-T) and the Galician Ministry of Education, University and Professional Training (grants ED431F 2018/02, ED431C 2018/29, ED431G/08, and ED431G2019/04), with all grants co-funded by the European Regional Development Fund (ERDF/FEDER program). Data collection and sharing for this project was funded by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). The ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie; Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Cogstate; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California. Publisher Copyright: © 2021, The Author(s).en
dc.description.statusPeer revieweden
dc.format.extent26
dc.format.extent5980955
dc.identifier281141454
dc.identifier3f020df5-de13-4ae9-947c-c641bfb6fab4
dc.identifier85100118850
dc.identifier33514817
dc.identifier.citationEl-Sappagh, S, Alonso, J M, Islam, S M R, Sultan, A M & Kwak, K S 2021, 'A multilayer multimodal detection and prediction model based on explainable artificial intelligence for Alzheimer’s disease', Scientific Reports, vol. 11, no. 1, 2660. https://doi.org/10.1038/s41598-021-82098-3en
dc.identifier.doi10.1038/s41598-021-82098-3
dc.identifier.iss1en
dc.identifier.issn2045-2322
dc.identifier.otherORCID: /0000-0003-2968-9561/work/143414254
dc.identifier.urihttps://hdl.handle.net/2164/21748
dc.identifier.urlhttp://www.scopus.com/inward/record.url?scp=85100118850&partnerID=8YFLogxKen
dc.identifier.vol11en
dc.language.isoeng
dc.relation.ispartofScientific Reportsen
dc.subjectQA75 Electronic computers. Computer scienceen
dc.subjectGeneralen
dc.subjectEuropean Commissionen
dc.subjectSupplementary Dataen
dc.subject.lccQA75en
dc.titleA multilayer multimodal detection and prediction model based on explainable artificial intelligence for Alzheimer’s diseaseen
dc.typeJournal articleen

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