{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T21:06:45Z","timestamp":1790802405589,"version":"4.1.0"},"reference-count":61,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62502163"],"award-info":[{"award-number":["62502163"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62473312"],"award-info":[{"award-number":["62473312"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62173271"],"award-info":[{"award-number":["62173271"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["clinicalkey.com","clinicalkey.com.au","clinicalkey.es","clinicalkey.fr","clinicalkey.jp","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Computer Methods and Programs in Biomedicine"],"published-print":{"date-parts":[[2026,12]]},"DOI":"10.1016\/j.cmpb.2026.109620","type":"journal-article","created":{"date-parts":[[2026,8,31]],"date-time":"2026-08-31T14:54:45Z","timestamp":1788188085000},"page":"109620","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Diagnosis-guided multimodal adversarial graph masked autoencoder network for association analysis between brain imaging and gene expression in Alzheimer\u2019s disease"],"prefix":"10.1016","volume":"287","author":[{"given":"Jia-Ni","family":"Li","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1305-7447","authenticated-orcid":false,"given":"Shao-Wu","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4595-6167","authenticated-orcid":false,"given":"Ting-He","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"1","key":"10.1016\/j.cmpb.2026.109620_b1","doi-asserted-by":"crossref","first-page":"5369","DOI":"10.1038\/s41467-021-25680-7","article-title":"Unified AI framework to uncover deep interrelationships between gene expression and alzheimer\u2019s disease neuropathologies","volume":"12","author":"Beebe-Wang","year":"2021","journal-title":"Nat. Commun."},{"key":"10.1016\/j.cmpb.2026.109620_b2","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2022.105634","article-title":"Early diagnosis of alzheimer\u2019s disease based on deep learning: A systematic review","volume":"146","author":"Fathi","year":"2022","journal-title":"Comput. Biol. Med."},{"issue":"6","key":"10.1016\/j.cmpb.2026.109620_b3","doi-asserted-by":"crossref","DOI":"10.1093\/bib\/bbab165","article-title":"MDA-GCNFTG: identifying mirna-disease associations based on graph convolutional networks via graph sampling through the feature and topology graph","volume":"22","author":"Chu","year":"2021","journal-title":"Brief. Bioinform."},{"issue":"7","key":"10.1016\/j.cmpb.2026.109620_b4","doi-asserted-by":"crossref","DOI":"10.1002\/advs.202205486","article-title":"Gene expression has distinct associations with brain structure and function in major depressive disorder","volume":"10","author":"Liu","year":"2023","journal-title":"Adv. Sci."},{"issue":"9","key":"10.1016\/j.cmpb.2026.109620_b5","doi-asserted-by":"crossref","first-page":"1961","DOI":"10.1016\/j.neurobiolaging.2014.03.031","article-title":"Brain gene expression patterns differentiate mild cognitive impairment from normal aged and alzheimer\u2019s disease","volume":"35","author":"Berchtold","year":"2014","journal-title":"Neurobiol. Aging"},{"key":"10.1016\/j.cmpb.2026.109620_b6","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2020.101656","article-title":"Detecting genetic associations with brain imaging phenotypes in alzheimer\u2019s disease via a novel structured SCCA approach","volume":"61","author":"Du","year":"2020","journal-title":"Med. Image Anal."},{"issue":"21","key":"10.1016\/j.cmpb.2026.109620_b7","doi-asserted-by":"crossref","first-page":"5214","DOI":"10.1093\/bioinformatics\/btaa650","article-title":"Alzheimer\u2019s disease neuroimaging initiative, co-sparse reduced-rank regression for association analysis between imaging phenotypes and genetic variants","volume":"36","author":"Wen","year":"2020","journal-title":"Bioinformatics"},{"key":"10.1016\/j.cmpb.2026.109620_b8","doi-asserted-by":"crossref","unstructured":"D. Xi, D. Cui, M. Zhang, J. Zhang, M. Shang, L. Guo, L. Du, J. Han, Identification of disease-related genetic variants and imaging factors leveraging summary statistics, in: 2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM, 2024, pp. 1244\u20131249.","DOI":"10.1109\/BIBM62325.2024.10822054"},{"issue":"9","key":"10.1016\/j.cmpb.2026.109620_b9","doi-asserted-by":"crossref","first-page":"2348","DOI":"10.1109\/TMI.2022.3162870","article-title":"A deep generative\u2013discriminative learning for multimodal representation in imaging genetics","volume":"41","author":"Ko","year":"2022","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.cmpb.2026.109620_b10","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2023.102842","article-title":"Deep multimodality-disentangled association analysis network for imaging genetics in neurodegenerative diseases","volume":"88","author":"Wang","year":"2023","journal-title":"Med. Image Anal."},{"issue":"6","key":"10.1016\/j.cmpb.2026.109620_b11","doi-asserted-by":"crossref","DOI":"10.1093\/bib\/bbac454","article-title":"A novel generation adversarial network framework with characteristics aggregation and diffusion for brain disease classification and feature selection","volume":"23","author":"Bi","year":"2022","journal-title":"Brief. Bioinform."},{"issue":"3","key":"10.1016\/j.cmpb.2026.109620_b12","doi-asserted-by":"crossref","DOI":"10.1093\/bib\/bbac137","article-title":"Feature aggregation graph convolutional network based on imaging genetic data for diagnosis and pathogeny identification of alzheimer\u2019s disease","volume":"23","author":"Bi","year":"2022","journal-title":"Brief. Bioinform."},{"issue":"6","key":"10.1016\/j.cmpb.2026.109620_b13","doi-asserted-by":"crossref","first-page":"3513","DOI":"10.1109\/JBHI.2024.3383885","article-title":"Deep self-reconstruction fusion similarity hashing for the diagnosis of alzheimer\u2019s disease on multi-modal data","volume":"28","author":"Wu","year":"2024","journal-title":"IEEE J. Biomed. Health Inform."},{"issue":"1","key":"10.1016\/j.cmpb.2026.109620_b14","doi-asserted-by":"crossref","first-page":"446","DOI":"10.1109\/JBHI.2021.3088342","article-title":"Predicting mirna-disease associations based on multi-view variational graph auto-encoder with matrix factorization","volume":"26","author":"Ding","year":"2022","journal-title":"IEEE J. Biomed. Health Inform."},{"issue":"4","key":"10.1016\/j.cmpb.2026.109620_b15","doi-asserted-by":"crossref","first-page":"433","DOI":"10.1002\/jnr.24364","article-title":"Optimization of rs-fMRI parameters in the seed correlation analysis (SCA) in DPARSF toolbox: A preliminary study","volume":"97","author":"Karpiel","year":"2019","journal-title":"J. Neurosci. Res."},{"key":"10.1016\/j.cmpb.2026.109620_b16","article-title":"Alzheimer\u2019s disease neuroimaging initiative, cortical graph neural network for AD and MCI diagnosis and transfer learning across populations","volume":"23","author":"Wee","year":"2019","journal-title":"NeuroImage: Clin."},{"issue":"1","key":"10.1016\/j.cmpb.2026.109620_b17","doi-asserted-by":"crossref","first-page":"3485","DOI":"10.1038\/s41598-020-60595-1","article-title":"Prediction of Alzheimer\u2019s disease using blood gene expression data","volume":"10","author":"Lee","year":"2020","journal-title":"Sci. Rep."},{"issue":"1","key":"10.1016\/j.cmpb.2026.109620_b18","article-title":"Integrative analysis of DNA methylation and gene expression identifies genes associated with biological aging in Alzheimer\u2019s disease","volume":"14","author":"Kim","year":"2022","journal-title":"Alzheimer\u2019s Dement.: Diagn. Assess. Dis. Monit."},{"issue":"9","key":"10.1016\/j.cmpb.2026.109620_b19","doi-asserted-by":"crossref","first-page":"5116","DOI":"10.1073\/pnas.091062498","article-title":"Significance analysis of microarrays applied to the ionizing radiation response","volume":"98","author":"Tusher","year":"2001","journal-title":"Proc. Natl. Acad. Sci."},{"issue":"6","key":"10.1016\/j.cmpb.2026.109620_b20","doi-asserted-by":"crossref","first-page":"2008","DOI":"10.1093\/brain\/awaf008","article-title":"Anterior-temporal network hyperconnectivity is key to Alzheimer\u2019s disease: from ageing to dementia","volume":"148","author":"Chauveau","year":"2025","journal-title":"Brain"},{"issue":"12","key":"10.1016\/j.cmpb.2026.109620_b21","doi-asserted-by":"crossref","DOI":"10.1002\/alz.70982","article-title":"Blood gene expression network expression strongly relates to brain amyloid burden","volume":"21","author":"Janve","year":"2025","journal-title":"Alzheimer\u2019s Dement."},{"issue":"4","key":"10.1016\/j.cmpb.2026.109620_b22","doi-asserted-by":"crossref","first-page":"2252","DOI":"10.1109\/TPAMI.2023.3330795","article-title":"Structure mapping generative adversarial network for multi-view information mapping pattern mining","volume":"46","author":"an Bi","year":"2024","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"1","key":"10.1016\/j.cmpb.2026.109620_b23","doi-asserted-by":"crossref","first-page":"479","DOI":"10.1109\/TCBB.2020.2999397","article-title":"Group sparse joint non-negative matrix factorization on orthogonal subspace for multi-modal imaging genetics data analysis","volume":"19","author":"Peng","year":"2022","journal-title":"IEEE\/ACM Trans. Comput. Biology Bioinform."},{"key":"10.1016\/j.cmpb.2026.109620_b24","doi-asserted-by":"crossref","unstructured":"Z. Hou, X. Liu, Y. Cen, Y. Dong, H. Yang, C. Wang, J. Tang, Graphmae: Self-supervised masked graph autoencoders, in: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2022, pp. 594\u2013604.","DOI":"10.1145\/3534678.3539321"},{"key":"10.1016\/j.cmpb.2026.109620_b25","first-page":"1024","article-title":"Inductive representation learning on large graphs","volume":"30","author":"Hamilton","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"issue":"3","key":"10.1016\/j.cmpb.2026.109620_b26","doi-asserted-by":"crossref","first-page":"1001","DOI":"10.1002\/hbm.24428","article-title":"Effective feature learning and fusion of multimodality data using stage-wise deep neural network for dementia diagnosis","volume":"40","author":"Zhou","year":"2019","journal-title":"Hum. Brain Mapp."},{"issue":"1","key":"10.1016\/j.cmpb.2026.109620_b27","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1109\/JPROC.2019.2947272","article-title":"Brain imaging genomics: integrated analysis and machine learning","volume":"108","author":"Shen","year":"2020","journal-title":"Proc. IEEE"},{"key":"10.1016\/j.cmpb.2026.109620_b28","first-page":"4126","article-title":"Full-gradient representation for neural network visualization","volume":"32","author":"Srinivas","year":"2019","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.cmpb.2026.109620_b29","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1016\/j.neurobiolaging.2018.04.009","article-title":"Alzheimer\u2019s disease neuroimaging initiative, classifying Alzheimer\u2019s disease with brain imaging and genetic data using a neural network framework","volume":"68","author":"Ning","year":"2018","journal-title":"Neurobiol. Aging"},{"issue":"8","key":"10.1016\/j.cmpb.2026.109620_b30","doi-asserted-by":"crossref","first-page":"2178","DOI":"10.1093\/bioinformatics\/btac088","article-title":"Multi-level attention graph neural network based on co-expression gene modules for disease diagnosis and prognosis","volume":"38","author":"Xing","year":"2022","journal-title":"Bioinformatics"},{"key":"10.1016\/j.cmpb.2026.109620_b31","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1186\/s12859-017-1543-x","article-title":"Sparse kernel canonical correlation analysis for discovery of nonlinear interactions in high-dimensional data","volume":"18","author":"Yoshida","year":"2017","journal-title":"BMC Bioinformatics"},{"issue":"5","key":"10.1016\/j.cmpb.2026.109620_b32","doi-asserted-by":"crossref","first-page":"3029","DOI":"10.1109\/JBHI.2024.3372294","article-title":"Diagnosis-guided deep subspace clustering association study for pathogenetic markers identification of alzheimer\u2019s disease based on comparative atlases","volume":"28","author":"Jiao","year":"2024","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"10.1016\/j.cmpb.2026.109620_b33","doi-asserted-by":"crossref","DOI":"10.1002\/alz.079343","article-title":"Alzheimer\u2019s disease neuroimaging initiative, amyloidosis at putamen predicts vulnerability to Alzheimer\u2019s disease","volume":"19","author":"Yang","year":"2023","journal-title":"Alzheimer\u2019s Dement."},{"issue":"9","key":"10.1016\/j.cmpb.2026.109620_b34","doi-asserted-by":"crossref","first-page":"e859","DOI":"10.1212\/WNL.0000000000006075","article-title":"Influence of tau PET, amyloid PET, and hippocampal volume on cognition in alzheimer disease","volume":"91","author":"Aschenbrenner","year":"2018","journal-title":"Neurology"},{"key":"10.1016\/j.cmpb.2026.109620_b35","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1186\/s13195-021-00811-w","article-title":"Glucose metabolism in the right middle temporal gyrus could be a potential biomarker for subjective cognitive decline: a study of a han population","volume":"13","author":"Dong","year":"2021","journal-title":"Alzheimer\u2019s Res. Ther."},{"key":"10.1016\/j.cmpb.2026.109620_b36","doi-asserted-by":"crossref","first-page":"265","DOI":"10.1007\/s00429-010-0283-8","article-title":"Atrophy in the parahippocampal gyrus as an early biomarker of Alzheimer\u2019s disease","volume":"215","author":"Ech\u00e1varri","year":"2011","journal-title":"Brain Struct. Funct."},{"key":"10.1016\/j.cmpb.2026.109620_b37","doi-asserted-by":"crossref","first-page":"1059","DOI":"10.3389\/fneur.2019.01059","article-title":"The association between distinct frontal brain volumes and behavioral symptoms in mild cognitive impairment","volume":"10","author":"Cajanus","year":"2019","journal-title":"Frontiers in Neurology"},{"issue":"2","key":"10.1016\/j.cmpb.2026.109620_b38","doi-asserted-by":"crossref","first-page":"390","DOI":"10.1148\/radiol.2021203383","article-title":"Cortical thickness from MRI to predict conversion from mild cognitive impairment to dementia in parkinson disease: a machine learning\u2013based model","volume":"300","author":"Shin","year":"2021","journal-title":"Radiology"},{"issue":"5","key":"10.1016\/j.cmpb.2026.109620_b39","doi-asserted-by":"crossref","first-page":"3629","DOI":"10.1002\/alz.13805","article-title":"The ABC\u2019s of Alzheimer risk gene ABCA7","volume":"20","author":"Duchateau","year":"2024","journal-title":"Alzheimer\u2019s Dement."},{"issue":"1","key":"10.1016\/j.cmpb.2026.109620_b40","doi-asserted-by":"crossref","first-page":"6726","DOI":"10.1038\/s41467-025-61650-z","article-title":"Machine learning in Alzheimer\u2019s disease genetics","volume":"16","author":"Bracher-Smith","year":"2025","journal-title":"Nat. Commun."},{"issue":"01","key":"10.1016\/j.cmpb.2026.109620_b41","first-page":"31","article-title":"WWOX is a risk factor for alzheimer\u2019s disease: How and why?","volume":"14","author":"Sze","year":"2020","journal-title":"Proc. Singap. Natl. Acad. Sci."},{"key":"10.1016\/j.cmpb.2026.109620_b42","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1016\/j.neuroscience.2019.10.006","article-title":"The interaction between contactin and amyloid precursor protein and its role in Alzheimer\u2019s disease","volume":"424","author":"Bamford","year":"2020","journal-title":"Neuroscience"},{"issue":"505","key":"10.1016\/j.cmpb.2026.109620_b43","doi-asserted-by":"crossref","DOI":"10.1126\/scitranslmed.aau2291","article-title":"The MS4A gene cluster is a key modulator of soluble TREM2 and Alzheimer\u2019s disease risk","volume":"11","author":"Deming","year":"2019","journal-title":"Sci. Transl. Med."},{"key":"10.1016\/j.cmpb.2026.109620_b44","doi-asserted-by":"crossref","DOI":"10.3389\/fgene.2021.638217","article-title":"The molecular function of PURA and its implications in neurological diseases","volume":"12","author":"Molitor","year":"2021","journal-title":"Front. Genet."},{"issue":"2","key":"10.1016\/j.cmpb.2026.109620_b45","doi-asserted-by":"crossref","first-page":"164","DOI":"10.1002\/alz.12220","article-title":"ABCA7 and the altered lipidostasis hypothesis of Alzheimer\u2019s disease","volume":"17","author":"Lyssenko","year":"2021","journal-title":"Alzheimer\u2019s Dement."},{"issue":"1","key":"10.1016\/j.cmpb.2026.109620_b46","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1186\/s13195-019-0563-3","article-title":"ABCA7 polymorphisms correlate with memory impairment and default mode network in patients with APOE \u025b4-associated alzheimer\u2019s disease","volume":"11","author":"Chang","year":"2019","journal-title":"Alzheimer\u2019s Res. Ther."},{"issue":"8","key":"10.1016\/j.cmpb.2026.109620_b47","article-title":"Metabolic network analysis reveals altered bile acid synthesis and metabolism in Alzheimer\u2019s disease","volume":"1","author":"Baloni","year":"2020","journal-title":"Cell Rep. Med."},{"issue":"2","key":"10.1016\/j.cmpb.2026.109620_b48","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1007\/s00401-019-01994-1","article-title":"The role of ABCA7 in Alzheimer\u2019s disease: evidence from genomics, transcriptomics and methylomics","volume":"138","author":"Roeck","year":"2019","journal-title":"Acta Neuropathol."},{"key":"10.1016\/j.cmpb.2026.109620_b49","doi-asserted-by":"crossref","unstructured":"Y. Li, Y. Liu, T. Wang, B. Lei, Alzheimer\u2019s Disease Prediction via the Association of Single Nucleotide Polymorphism with Brain Regions, in: Biometric Recognition, 2021, pp. 102\u2013111.","DOI":"10.1007\/978-3-030-86608-2_12"},{"issue":"1","key":"10.1016\/j.cmpb.2026.109620_b50","first-page":"10","article-title":"Enrichment of disease-associated genes in cortical areas defined by transcriptome-based parcellation","volume":"7","author":"Gryglewski","year":"2022","journal-title":"Biological Psychiatry: Cogn. Neurosci. Neuroimaging"},{"issue":"7","key":"10.1016\/j.cmpb.2026.109620_b51","doi-asserted-by":"crossref","first-page":"9277","DOI":"10.18632\/aging.202950","article-title":"Multiomics integrative analysis identifies APOE allele-specific blood biomarkers associated to Alzheimer\u2019s disease etiopathogenesis","volume":"13","author":"Madrid","year":"2021","journal-title":"Aging (Albany NY)"},{"issue":"3","key":"10.1016\/j.cmpb.2026.109620_b52","doi-asserted-by":"crossref","first-page":"400","DOI":"10.1177\/1535370214561952","article-title":"The fragile site WWOX gene and the developing brain","volume":"240","author":"Tabarki","year":"2015","journal-title":"Exp. Biol. Med."},{"issue":"4","key":"10.1016\/j.cmpb.2026.109620_b53","doi-asserted-by":"crossref","first-page":"824","DOI":"10.3390\/cells10040824","article-title":"Neurological disorders associated with WWOX germline mutations\u2014a comprehensive overview","volume":"10","author":"Banne","year":"2021","journal-title":"Cells"},{"issue":"2","key":"10.1016\/j.cmpb.2026.109620_b54","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1016\/j.tins.2020.10.011","article-title":"Regulation of GABAARs by transmembrane accessory proteins","volume":"44","author":"Han","year":"2021","journal-title":"Trends Neurosci."},{"key":"10.1016\/j.cmpb.2026.109620_b55","series-title":"Multi-omics analysis reveals aspirin is associated with reduced risk of Alzheimer\u2019s disease","author":"Grabowska","year":"2025"},{"issue":"6","key":"10.1016\/j.cmpb.2026.109620_b56","doi-asserted-by":"crossref","first-page":"929","DOI":"10.1002\/jnr.20806","article-title":"Role of pur\u03b1 in targeting mRNA to sites of translation in hippocampal neuronal dendrites","volume":"83","author":"Johnson","year":"2006","journal-title":"J. Neurosci. Res."},{"issue":"4","key":"10.1016\/j.cmpb.2026.109620_b57","doi-asserted-by":"crossref","first-page":"1635","DOI":"10.3233\/JAD-181085","article-title":"A meta-analysis of Alzheimer\u2019s disease brain transcriptomic data","volume":"68","author":"Patel","year":"2019","journal-title":"J. Alzheimer\u2019s Dis."},{"issue":"2","key":"10.1016\/j.cmpb.2026.109620_b58","doi-asserted-by":"crossref","first-page":"130","DOI":"10.1002\/cne.10068","article-title":"Spatiotemporal expression patterns of slit and robo genes in the rat brain","volume":"442","author":"Marillat","year":"2002","journal-title":"J. Comp. Neurol."},{"issue":"13","key":"10.1016\/j.cmpb.2026.109620_b59","doi-asserted-by":"crossref","first-page":"3395","DOI":"10.1523\/JNEUROSCI.4605-06.2007","article-title":"Robo1 and Robo2 cooperate to control the guidance of major axonal tracts in the mammalian forebrain","volume":"27","author":"L\u00f3pez-Bendito","year":"2007","journal-title":"J. Neurosci."},{"key":"10.1016\/j.cmpb.2026.109620_b60","doi-asserted-by":"crossref","unstructured":"M. Verleysen, D. Fran\u00e7ois, The curse of dimensionality in data mining and time series prediction, in: Computational Intelligence and Bioinspired Systems, 2005, pp. 758\u2013770.","DOI":"10.1007\/11494669_93"},{"issue":"2","key":"10.1016\/j.cmpb.2026.109620_b61","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1177\/0268580905052366","article-title":"When less is more: Selection problems in large-N and small-N cross-national comparisons","volume":"20","author":"Ebbinghaus","year":"2005","journal-title":"Int. Sociol."}],"container-title":["Computer Methods and Programs in Biomedicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S016926072600369X?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S016926072600369X?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T20:29:23Z","timestamp":1790800163000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S016926072600369X"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,12]]},"references-count":61,"alternative-id":["S016926072600369X"],"URL":"https:\/\/doi.org\/10.1016\/j.cmpb.2026.109620","relation":{},"ISSN":["0169-2607"],"issn-type":[{"value":"0169-2607","type":"print"}],"subject":[],"published":{"date-parts":[[2026,12]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Diagnosis-guided multimodal adversarial graph masked autoencoder network for association analysis between brain imaging and gene expression in Alzheimer\u2019s disease","name":"articletitle","label":"Article Title"},{"value":"Computer Methods and Programs in Biomedicine","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.cmpb.2026.109620","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"109620"}}