Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/111727
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dc.contributorSchool of Optometry-
dc.creatorZhang, Xen_US
dc.creatorLi, Den_US
dc.creatorYe, Sen_US
dc.creatorLiu, Sen_US
dc.creatorMa, Sen_US
dc.creatorLi, Men_US
dc.creatorPeng, Qen_US
dc.creatorHu, Len_US
dc.creatorShang, Xen_US
dc.creatorHe, Men_US
dc.creatorZhang, Len_US
dc.date.accessioned2025-03-13T02:25:01Z-
dc.date.available2025-03-13T02:25:01Z-
dc.identifier.urihttp://hdl.handle.net/10397/111727-
dc.language.isoenen_US
dc.publisherBioMed Central Ltd.en_US
dc.rights© The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.en_US
dc.rightsThe following publication Zhang, X., Li, D., Ye, S. et al. Decoding the genetic comorbidity network of Alzheimer's disease. BioData Mining 17, 40 (2024) is available at https://doi.org/10.1186/s13040-024-00394-w.en_US
dc.titleDecoding the genetic comorbidity network of alzheimer's diseaseen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.volume17en_US
dc.identifier.doi10.1186/s13040-024-00394-wen_US
dcterms.abstractAlzheimer's disease (AD) has emerged as the most prevalent and complex neurodegenerative disorder among the elderly population. However, the genetic comorbidity etiology for AD remains poorly understood. In this study, we conducted pleiotropic analysis for 41 AD phenotypic comorbidities, identifying ten genetic comorbidities with 16 pleiotropy genes associated with AD. Through biological functional and network analysis, we elucidated the molecular and functional landscape of AD genetic comorbidities. Furthermore, leveraging the pleiotropic genes and reported biomarkers for AD genetic comorbidities, we identified 50 potential biomarkers for AD diagnosis. Our findings deepen the understanding of the occurrence of AD genetic comorbidities and provide new insights for the search for AD diagnostic markers. Graphical Abstract: Study pipeline.-
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationBioData mining, 2024, v. 17, 40en_US
dcterms.isPartOfBioData miningen_US
dcterms.issued2024-
dc.identifier.scopus2-s2.0-85206249496-
dc.identifier.eissn1756-0381en_US
dc.identifier.artn40en_US
dc.description.validate202502 bcrc-
dc.description.oaVersion of Recorden_US
dc.identifier.FolderNumberOA_Scopus/WOS-
dc.description.fundingSourceOthersen_US
dc.description.fundingTextNational Natural Science Foundation of China; GDPH Supporting Fund for Talent Program;Guangdong Provincial People’s Hospital, the Guangdong Provincial Medical Research Fund; Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Applicationen_US
dc.description.pubStatusPublisheden_US
dc.description.oaCategoryCCen_US
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