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AD pathology is associated with extracellular amyloid-\u03b2 plaques, intracellular tau neurofibrillary tangles, synaptic dysfunction, and neuronal loss. AD accounts for approximately 60\u201380% of dementia cases globally. In 2022, AD was the seventh leading cause of death in the United States, and the number of Americans aged 65 and older living with Alzheimer\u2019s dementia is projected to increase substantially by 2060. Despite decades of research, AD\/ADRD data resources remain fragmented across clinical, imaging, genetic, genomic, and therapeutic domains. This paper addresses that gap by providing a centralized review of widely used AD\/ADRD databases and computational methods. We first summarize computational approaches used to analyze these datasets, including machine learning (ML), natural language processing (NLP), and biomedical imaging. We then review eight databases classified into three categories: Clinical and Population Data, Genetics and Genomics, and Drug Discovery and Therapeutics. Finally, we discuss real-world applications, including early diagnosis, clinical decision support, personalized medicine, and drug-mechanism analysis. This review identifies opportunities for future work in data harmonization, cross-database compatibility, and robust, generalizable AI models for AD\/ADRD research.<\/jats:p>","DOI":"10.3390\/bioengineering13060698","type":"journal-article","created":{"date-parts":[[2026,6,18]],"date-time":"2026-06-18T08:59:38Z","timestamp":1781773178000},"page":"698","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Centralized Review of Alzheimer\u2019s Disease and Related Dementias Biomedical Repositories and Computational Methods"],"prefix":"10.3390","volume":"13","author":[{"given":"Johaan","family":"Kathilankal Jis","sequence":"first","affiliation":[{"name":"College of Computing and Software Engineering, Kennesaw State University, Marietta, GA 30060, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8497-3069","authenticated-orcid":false,"given":"Kewei","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Health Solutions, Arizona State University, Phoenix, AZ 85004, USA"},{"name":"Bateman Physical Sciences Center, Arizona State University, Tempe, AZ 85281, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5782-3329","authenticated-orcid":false,"given":"Chen","family":"Zhao","sequence":"additional","affiliation":[{"name":"College of Computing and Software Engineering, Kennesaw State University, Marietta, GA 30060, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8345-2250","authenticated-orcid":false,"given":"Lingtao","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Computing and Software Engineering, Kennesaw State University, Marietta, GA 30060, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5746-2914","authenticated-orcid":false,"given":"Seyedamin","family":"Pouriyeh","sequence":"additional","affiliation":[{"name":"College of Computing and Software Engineering, Kennesaw State University, Marietta, GA 30060, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zongxing","family":"Xie","sequence":"additional","affiliation":[{"name":"College of Computing and Software Engineering, Kennesaw State University, Marietta, GA 30060, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4436-3474","authenticated-orcid":false,"given":"Yixin","family":"Xie","sequence":"additional","affiliation":[{"name":"College of Computing and Software Engineering, Kennesaw State University, Marietta, GA 30060, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,6,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"168","DOI":"10.1097\/JGP.0b013e31816029ec","article-title":"In-creased neurofibrillary tangles in patients with alzheimer disease with comorbid depression","volume":"16","author":"Rapp","year":"2008","journal-title":"Am. J. Geriatr. Psychiatry"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"187","DOI":"10.2174\/1568026619666190201153257","article-title":"Small molecule natural products and alzheimer\u2019s disease","volume":"19","author":"Wu","year":"2019","journal-title":"Curr. Top. Med. Chem."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"e2951","DOI":"10.1212\/WNL.0000000000011044","article-title":"How a buildup of abnormal proteins in the brain may be the key to understanding alzheimer disease","volume":"95","author":"Karceski","year":"2020","journal-title":"Neurology"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"199","DOI":"10.2147\/JIR.S86958","article-title":"Targeting neuroinflammation in alzheimer\u2019s disease","volume":"9","author":"Bronzuoli","year":"2016","journal-title":"J. Inflamm. Res."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"3306","DOI":"10.2174\/092986711796504664","article-title":"The \u201caged garlic extract\u201d (age) and one of its active ingredients s-allyl-lcysteine (sac) as potential preventive and therapeutic agents for alzheimer\u2019s disease (ad)","volume":"18","author":"Ray","year":"2011","journal-title":"Curr. Med. Chem."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"571","DOI":"10.1007\/s00401-011-0826-y","article-title":"Alzheimer\u2019s disease is not \u201cbrain aging\u201d: Neuropathological, genetic, and epidemiological human studies","volume":"121","author":"Nelson","year":"2011","journal-title":"Acta Neuropathol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"651","DOI":"10.1016\/j.neurobiolaging.2003.12.024","article-title":"Apolipoprotein gene and its interaction with the environmentally driven risk factors: Molecular, genetic and epidemiological studies of alzheimer\u2019s disease","volume":"25","author":"Lahiri","year":"2004","journal-title":"Neurobiol. Aging"},{"key":"ref_8","unstructured":"Stern, P.C., and Feller, I. (2007). A Strategy for Assessing Science: Behavioral and Social Research on Aging, National Academies Press."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"234","DOI":"10.1016\/j.jalz.2012.04.004","article-title":"The draft \u201cnational plan\u201d to address alzheimer\u2019s disease-national alzheimer\u2019s project act (napa)","volume":"8","author":"Khachaturian","year":"2012","journal-title":"Alzheimer\u2019s Dement."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Wang, H., Sun, M., Li, W., Liu, X., Zhu, M., and Qin, H. (2023). Biomarkers associated with the pathogenesis of alzheimer\u2019s disease. Front. Cell. Neurosci., 17.","DOI":"10.3389\/fncel.2023.1279046"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"2637","DOI":"10.1038\/s41598-024-51985-w","article-title":"An explainable machine learning approach for alzheimer\u2019s disease classification","volume":"14","author":"Alatrany","year":"2024","journal-title":"Sci. Rep."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"e60866","DOI":"10.2196\/60866","article-title":"Improved alzheimer disease diagnosis with a machine learning approach and neu-roimaging: Case study development","volume":"6","author":"Lazli","year":"2025","journal-title":"JMIRx Med."},{"key":"ref_13","first-page":"e70082","article-title":"Natural language processing in alzheimer\u2019s disease research: Sys-tematic review of methods, data, and efficacy","volume":"17","author":"Shakeri","year":"2025","journal-title":"Alzheimer\u2019s Dement. Diagn. Assess. Dis. Monit."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"ooad014","DOI":"10.1093\/jamiaopen\/ooad014","article-title":"Extraction of clinical phenotypes for alzheimer\u2019s disease dementia from clinical notes using natural language processing","volume":"6","author":"Oh","year":"2023","journal-title":"JAMIA Open"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s40708-025-00252-3","article-title":"Machine-learning models for alzheimer\u2019s disease di-agnosis using neuroimaging data: Survey, reproducibility, and generalizability evaluation","volume":"12","author":"Aghdam","year":"2025","journal-title":"Brain Inform."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"398","DOI":"10.1016\/j.neuroimage.2014.10.002","article-title":"Alzheimer\u2019s Disease Neuroimaging Initiative. Machine learning framework for early mri-based alzheimer\u2019s conversion prediction in mci subjects","volume":"104","author":"Moradi","year":"2015","journal-title":"Neuroimage"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"509","DOI":"10.2174\/1567205013666151116141705","article-title":"Frontiers for the early diagnosis of ad by means of mri brain imaging and support vector machines","volume":"13","author":"Salvatore","year":"2016","journal-title":"Curr. Alzheimer Res."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"S57","DOI":"10.1016\/S1053-8119(09)70228-9","article-title":"Svm classification of patients with alzheimer\u2019s disease and mild cognitive impairment using hip-pocampal shape features","volume":"47","author":"Gerardin","year":"2009","journal-title":"NeuroImage"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"2405","DOI":"10.1038\/s41598-022-06444-9","article-title":"Predictive classification of alzheimer\u2019s disease using brain imaging and genetic data","volume":"12","author":"Sheng","year":"2022","journal-title":"Sci. Rep."},{"key":"ref_20","first-page":"103526","article-title":"Enhancing alzheimer\u2019s disease prediction using random forest: A novel framework combining backward feature elimination and ant colony optimization","volume":"73","author":"Soladoye","year":"2025","journal-title":"Curr. Res. Transl. Med."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Tyralis, H., Papacharalampous, G., and Langousis, A. (2019). A brief review of random forests for water scientists and practitioners and their recent history in water resources. Water, 11.","DOI":"10.3390\/w11050910"},{"key":"ref_22","first-page":"100957","article-title":"Evaluation of machine learning models for the prediction of alzheimer\u2019s: In search of the best performance","volume":"44","year":"2025","journal-title":"Brain Behav. Immun.-Health"},{"key":"ref_23","first-page":"15","article-title":"Alzheimer disease forecasting using machine learning algorithm","volume":"13","author":"Malavika","year":"2020","journal-title":"Biosc. Biotech. Res. Comm. Spec. Issue"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"22381","DOI":"10.1038\/s41598-022-26958-6","article-title":"A new strategy for the early detection of alzheimer disease stages using multifractal geometry analysis based on k-nearest neighbor algorithm","volume":"12","author":"Elgammal","year":"2022","journal-title":"Sci. Rep."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"106930","DOI":"10.1016\/j.compbiomed.2023.106930","article-title":"Effective detection of alzheimer\u2019s disease by optimizing fuzzy k-nearest neighbors based on salp swarm algorithm","volume":"159","author":"Lu","year":"2023","journal-title":"Comput. Biol. Med."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"3404","DOI":"10.1038\/s41467-022-31037-5","article-title":"Multimodal deep learning for alzheimer\u2019s disease dementia assessment","volume":"13","author":"Qiu","year":"2022","journal-title":"Nat. Commun."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"377","DOI":"10.3390\/genes16040377","article-title":"Development of a k-nearest neighbors model for the prediction of late-onset alzheimer\u2019s risk by combining polygenic risk scores and phenotypic variables","volume":"16","author":"Ferreiro","year":"2025","journal-title":"Genes"},{"key":"ref_28","first-page":"190","article-title":"A review of the logistic regression model with emphasis on medical research","volume":"7","author":"Boateng","year":"2019","journal-title":"J. Data Anal. Inf. Process."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"929","DOI":"10.1002\/ana.24647","article-title":"White matter hyperintensities are a core feature of alzheimer\u2019s disease: Evidence from the dominantly inherited alzheimer network","volume":"79","author":"Lee","year":"2016","journal-title":"Ann. Neurol."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"614","DOI":"10.1016\/S1474-4422(14)70090-0","article-title":"Advancing research diagnostic criteria for alzheimer\u2019s disease: The iwg-2 criteria","volume":"13","author":"Dubois","year":"2014","journal-title":"Lancet Neurol."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1159\/000115751","article-title":"The mayo clinic study of aging: Design and sampling, participation, baseline measures and sample characteristics","volume":"30","author":"Roberts","year":"2008","journal-title":"Neuroepidemiology"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"672","DOI":"10.1017\/S1041610209009405","article-title":"The australian imaging, biomarkers and lifestyle (aibl) study of aging: Methodology and baseline characteristics of 1112 individuals recruited for a longitudinal study of alzheimer\u2019s disease","volume":"21","author":"Ellis","year":"2009","journal-title":"Int. Psychogeriatr."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1212\/WNL.0b013e3181cb3e25","article-title":"Alzheimer\u2019s disease neuroimaging initiative (adni) clinical characterization","volume":"74","author":"Petersen","year":"2010","journal-title":"Neurology"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1717","DOI":"10.1001\/jama.2021.3854","article-title":"Evaluation of aducanumab for alzheimer disease: Scientific evidence and regulatory review involving efficacy, safety, and futility","volume":"325","author":"Alexander","year":"2021","journal-title":"Jama"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1186\/s13195-022-00982-0","article-title":"A multi-regression framework to improve diagnostic ability of optical coherence tomography retinal biomarkers to discriminate mild cognitive impairment and alzheimer\u2019s disease","volume":"14","author":"Chua","year":"2022","journal-title":"Alzheimer\u2019s Res. Ther."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"497","DOI":"10.1016\/j.ophtha.2018.08.009","article-title":"Spectral-domain oct measurements in alzheimer\u2019s disease: A systematic review and meta-analysis","volume":"126","author":"Chan","year":"2019","journal-title":"Ophthalmology"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"45","DOI":"10.3233\/JAD-141659","article-title":"Retinal ganglion cell analysis using high-definition optical coherence tomography in patients with mild cognitive impairment and alzheimer\u2019s disease","volume":"45","author":"Cheung","year":"2015","journal-title":"J. Alzheimer\u2019s Dis."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"388","DOI":"10.1136\/bjophthalmol-2020-317659","article-title":"Convolutional neural network to identify symptomatic alzheimer\u2019s disease using multimodal retinal imaging","volume":"106","author":"Wisely","year":"2022","journal-title":"Br. J. Ophthalmol."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"666","DOI":"10.1016\/j.oret.2024.01.019","article-title":"Deep learning models for the screening of cognitive impairment using multimodal fundus images","volume":"8","author":"Shi","year":"2024","journal-title":"Ophthalmol. Retin."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"2206","DOI":"10.1111\/cns.13963","article-title":"Machine learning based on optical coherence tomography images as a diagnostic tool for alzheimer\u2019s disease","volume":"28","author":"Wang","year":"2022","journal-title":"CNS Neurosci. Ther."},{"key":"ref_41","first-page":"e70041","article-title":"Utilizing deep learning to predict alzheimer\u2019s disease and mild cognitive impairment with optical coherence tomography","volume":"17","author":"Chua","year":"2025","journal-title":"Alzheimer\u2019s Dement. Diagn. Assess. Dis. Monit."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"4184","DOI":"10.1167\/iovs.12-10420","article-title":"Reproducibility of diabetic macular edema estimates from sd-oct is affected by the choice of image analysis algorithm","volume":"54","author":"Sohn","year":"2013","journal-title":"Investig. Ophthalmol. Vis. Sci."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Jung, D.H., Son, G., Kwon, O.-H., Chang, K.-A., and Moon, C. (2022). Non-invasive nasal discharge fluid and other body fluid biomarkers in alzheimer\u2019s disease. Pharmaceutics, 14.","DOI":"10.3390\/pharmaceutics14081532"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Wu, C.-Y., Ho, C.-Y., and Yang, Y.-H. (2023). Developing biomarkers for the skin: Biomarkers for the diagnosis and prediction of treatment outcomes of alzheimer\u2019s disease. Int. J. Mol. Sci., 24.","DOI":"10.3390\/ijms24108478"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"7525","DOI":"10.1007\/s00415-024-12698-2","article-title":"Aquaporin4-igg seropositivity significantly increases the risk of comorbid autoimmune diseases in nmosd patients: Population-based registry data","volume":"271","author":"Pekmezovic","year":"2024","journal-title":"J. Neurol."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"252","DOI":"10.1007\/s00415-025-12996-3","article-title":"Blood phos-phorylated tau217 distinguishes amyloid-positive from amyloid-negative subjects in the alzheimer\u2019s disease continuum. A systematic review and meta-analysis","volume":"272","author":"Antonioni","year":"2025","journal-title":"J. Neurol."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1016\/0022-3956(75)90026-6","article-title":"Mini-mental state: A practical method for grading the cognitive state of patients for the clinician","volume":"12","author":"Folstein","year":"1975","journal-title":"J. Psychiatr. Res."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"695","DOI":"10.1111\/j.1532-5415.2005.53221.x","article-title":"The montreal cognitive assessment, MoCA: A brief screening tool for mild cognitive impairment","volume":"53","author":"Nasreddine","year":"2005","journal-title":"J. Am. Geriatr. Soc."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"2412","DOI":"10.1212\/WNL.43.11.2412-a","article-title":"The clinical dementia rating (CDR): Current version and scoring rules","volume":"43","author":"Morris","year":"1993","journal-title":"Neurology"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"1356","DOI":"10.1176\/ajp.141.11.1356","article-title":"A new rating scale for alzheimer\u2019s disease","volume":"141","author":"Rosen","year":"1984","journal-title":"Am. J. Psychiatry"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"323","DOI":"10.1093\/geronj\/37.3.323","article-title":"Measurement of functional activities in older adults in the community","volume":"37","author":"Pfeffer","year":"1982","journal-title":"J. Gerontol."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"2308","DOI":"10.1212\/WNL.44.12.2308","article-title":"The neuropsychiatric inventory: Comprehensive assessment of psychopathology in dementia","volume":"44","author":"Cummings","year":"1994","journal-title":"Neurology"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1016\/0022-3956(82)90033-4","article-title":"Devel-opment and validation of a geriatric depression screening scale: A preliminary report","volume":"17","author":"Yesavage","year":"1982","journal-title":"J. Psychiatr. Res."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"531","DOI":"10.1037\/0894-4105.22.4.531","article-title":"The measurement of everyday cognition (ECog): Scale development and psychometric properties","volume":"22","author":"Farias","year":"2008","journal-title":"Neuropsychology"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1097\/WAD.0b013e318191c7dd","article-title":"The alzheimer\u2019s disease centers\u2019 uniform data set (uds): The neuropsychologic test battery","volume":"23","author":"Weintraub","year":"2009","journal-title":"Alzheimer Dis. Assoc. Disord."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"502","DOI":"10.1007\/s11682-012-9186-z","article-title":"Development and assessment of a composite score for memory in the alzheimer\u2019s disease neuroimaging initiative (adni)","volume":"6","author":"Crane","year":"2012","journal-title":"Brain Imaging Behav."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"S54","DOI":"10.1212\/WNL.0b013e3182872ded","article-title":"Cognition assessment using the NIH toolbox","volume":"80","author":"Weintraub","year":"2013","journal-title":"Neurology"},{"key":"ref_58","first-page":"266","article-title":"Cambridge neuropsychological test automated battery (CANTAB): A factor analytic study of a large sample of normal elderly volunteers","volume":"5","author":"Robbins","year":"1994","journal-title":"Dementia"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Lyall, D.M., Cullen, B., Allerhand, M., Smith, D.J., Mackay, D., Evans, J., Anderson, J., Fawns-Ritchie, C., McIntosh, A.M., and Deary, I.J. (2016). Cognitive test scores in UK biobank: Data reduction in 480,416 participants and longitudinal stability in 20,346 participants. PLoS ONE, 11.","DOI":"10.1371\/journal.pone.0156366"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"7232","DOI":"10.1002\/alz.14234","article-title":"The adni4 digital study: A novel approach to recruitment, screening, and assessment of participants for ad clinical research","volume":"20","author":"Miller","year":"2024","journal-title":"Alzheimer\u2019s Dement."},{"key":"ref_61","first-page":"3079","article-title":"Semi-supervised sequence learning","volume":"28","author":"Dai","year":"2015","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_62","unstructured":"Dolan, B., and Brockett, C. (2005). Automatically constructing a corpus of sentential paraphrases. Third international Workshop on Paraphrasing (IWP2005), Asia Federation of Natural Language Processing."},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Bowman, S.R., Angeli, G., Potts, C., and Manning, C.D. (2015). A large annotated corpus for learning natural language inference. arXiv.","DOI":"10.18653\/v1\/D15-1075"},{"key":"ref_64","unstructured":"Radford, A., Narasimhan, K., Salimans, T., and Sutskever, I. (2026, April 10). Improving Language Understanding by Generative Pre-Training. Available online: https:\/\/cdn.openai.com\/research-covers\/language-unsupervised\/language_understanding_paper.pdf."},{"key":"ref_65","first-page":"2227","article-title":"Deep contextualized word representations","volume":"Volume 1","author":"Peters","year":"2018","journal-title":"Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies; Long Papers"},{"key":"ref_66","first-page":"4171","article-title":"Bert: Pre-training of deep bidirectional transformers for language understanding","volume":"Volume 1","author":"Devlin","year":"2019","journal-title":"Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies"},{"key":"ref_67","first-page":"5998","article-title":"Attention is all you need","volume":"30","author":"Vaswani","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_68","first-page":"415","article-title":"\u201ccloze procedure\u201d: A new tool for measuring readability","volume":"30","author":"Taylor","year":"1953","journal-title":"Journal. Mass Commun. Q."},{"key":"ref_69","unstructured":"Huang, K., Altosaar, J., and Ranganath, R. (2019). Clinicalbert: Modeling clinical notes and predicting hospital readmission. arXiv."},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Peng, Y., Yan, S., and Lu, Z. (2019). Transfer learning in biomedical natural language processing: An evaluation of BERT and ELMo on ten benchmarking datasets. Proceedings of the 18th BioNLP Workshop and Shared Task, (Florence, Italy), Association for Computational Linguistics.","DOI":"10.18653\/v1\/W19-5006"},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"1234","DOI":"10.1093\/bioinformatics\/btz682","article-title":"Biobert: A pre-trained biomedical language representation model for biomedical text mining","volume":"36","author":"Lee","year":"2020","journal-title":"Bioinformatics"},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"104442","DOI":"10.1016\/j.jbi.2023.104442","article-title":"Ad-bert: Using pre-trained language model to predict the progres-sion from mild cognitive impairment to alzheimer\u2019s disease","volume":"144","author":"Mao","year":"2023","journal-title":"J. Biomed. Inform."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"541","DOI":"10.1162\/neco.1989.1.4.541","article-title":"Backpropagation applied to handwritten zip code recognition","volume":"1","author":"LeCun","year":"1989","journal-title":"Neural Comput."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"7068349","DOI":"10.1155\/2018\/7068349","article-title":"Deep learning for computer vision: A brief review","volume":"2018","author":"Voulodimos","year":"2018","journal-title":"Comput. Intell. Neurosci."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"230","DOI":"10.1097\/WCO.0000000000001035","article-title":"Overview of tau pet molecular imaging","volume":"35","author":"Petersen","year":"2022","journal-title":"Curr. Opin. Neurol."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"845","DOI":"10.1001\/jamaneurol.2024.1612","article-title":"Tau positron emission tomography for predicting dementia in individuals with mild cognitive impairment","volume":"81","author":"Groot","year":"2024","journal-title":"JAMA Neurol."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"2700316","DOI":"10.1109\/JTEHM.2022.3177710","article-title":"Deep CNN-LSTM With Self-Attention Model for Human Activity Recognition Using Wearable Sensor","volume":"10","author":"Khatun","year":"2022","journal-title":"IEEE J. Transl. Eng. Health Med."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"103219","DOI":"10.1016\/j.cviu.2021.103219","article-title":"Skeleton-based action recognition via spatial and temporal transformer networks","volume":"208","author":"Plizzari","year":"2021","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_79","unstructured":"Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., and Gelly, S. (2021, January 4). An image is worth 16x16 words: Transformers for image recognition at scale. Proceedings of the International Conference on Learning Representations, Vienna, Austria."},{"key":"ref_80","doi-asserted-by":"crossref","unstructured":"Hu, X., Shen, X., Sun, Y., Shan, X., Min, W., Su, L., Fan, X., Elazab, A., Ge, R., and Wang, C. (2025). Itcfn: Incomplete triple-modal co-attention fusion network for mild cognitive impairment conver-sion prediction. arXiv.","DOI":"10.1109\/ISBI60581.2025.10980706"},{"key":"ref_81","doi-asserted-by":"crossref","unstructured":"Raza, M.L., Hassan, S.T., Jamil, S., Hyder, N., Batool, K., Walji, S., and Abbas, M.K. (2025). Advance-ments in deep learning for early diagnosis of alzheimer\u2019s disease using multimodal neuroimaging: Challenges and future directions. Front. Neuroinform., 19.","DOI":"10.3389\/fninf.2025.1557177"},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"2467083","DOI":"10.1080\/21642583.2025.2467083","article-title":"A systematic literature review on incom-plete multimodal learning: Techniques and challenges","volume":"13","author":"Zhan","year":"2025","journal-title":"Syst. Sci. Control Eng."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.media.2017.07.005","article-title":"A survey on deep learning in medical image analysis","volume":"42","author":"Litjens","year":"2017","journal-title":"Med. Image Anal."},{"key":"ref_84","unstructured":"Li, R., Zhang, W., Suk, H.-I., Wang, L., Li, J., Shen, D., and Ji, S. (2014). Deep learning based imaging data completion for improved brain disease diagnosis. Medical Image Computing and Computer-Assisted Intervention\u2013MICCAI 2014: 17th International Conference, Boston, MA, USA, 14\u201318 September 2014, Springer. Proceedings, Part III 17."},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"805","DOI":"10.1006\/nimg.2000.0582","article-title":"Voxel-based morphometry\u2014The methods","volume":"11","author":"Ashburner","year":"2000","journal-title":"Neuroimage"},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1002\/hbm.10062","article-title":"Fast robust automated brain extraction","volume":"17","author":"Smith","year":"2002","journal-title":"Hum. Brain Mapp."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1007\/s12194-017-0406-5","article-title":"Overview of deep learning in medical imaging","volume":"10","author":"Suzuki","year":"2017","journal-title":"Radiol. Phys. Technol."},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1186\/alzrt106","article-title":"Resting-state fmri as a biomarker for alzheimer\u2019s disease?","volume":"4","author":"Damoiseaux","year":"2012","journal-title":"Alzheimer\u2019s Res. Ther."},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"1564","DOI":"10.1016\/j.neurobiolaging.2011.06.007","article-title":"Resting state fmri in alzheimer\u2019s disease: Beyond the default mode network","volume":"33","author":"Agosta","year":"2012","journal-title":"Neurobiol. Aging"},{"key":"ref_90","first-page":"73","article-title":"Resting-state network dysfunction in alzheimer\u2019s disease: A systematic review and meta-analysis","volume":"8","author":"Badhwar","year":"2017","journal-title":"Alzheimer\u2019s Dement. Diagn. Assess. Dis. Monit."},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"3305","DOI":"10.1002\/hbm.25436","article-title":"Distributed functional connectivity predicts neuropsychological test performance among older adults","volume":"42","author":"Kwak","year":"2021","journal-title":"Hum. Brain Mapp."},{"key":"ref_92","first-page":"843","article-title":"Functional magnetic resonance imaging, deep learning, and alzheimer\u2019s disease: A systematic review","volume":"93","author":"Warren","year":"2023","journal-title":"J. Alzheimer\u2019s Dis."},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"5514839","DOI":"10.1155\/2021\/5514839","article-title":"Diagnosis of alzheimer\u2019s disease sever-ity with fmri images using robust multitask feature extraction method and convolutional neural network (cnn)","volume":"2021","author":"Amini","year":"2021","journal-title":"Comput. Math. Methods Med."},{"key":"ref_94","doi-asserted-by":"crossref","unstructured":"Lin, Q., Rosenberg, M.D., Yoo, K.H., Hsu, T.-W., O\u2019Connell, T.P., and Chun, M.M. (2022). Convolutional recurrent neural network for dynamic functional mri analysis and brain disease identification. Front. Neurosci., 16.","DOI":"10.3389\/fnins.2022.933660"},{"key":"ref_95","doi-asserted-by":"crossref","unstructured":"Kubben, P., Dumontier, M., and Dekker, A. (2019). Fundamentals of Clinical Data Science, Springer.","DOI":"10.1007\/978-3-319-99713-1"},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12911-016-0239-x","article-title":"The value of structured data elements from electronic health records for identifying subjects for primary care clinical trials","volume":"16","author":"Ateya","year":"2015","journal-title":"BMC Med. Inform. Decis. Mak."},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"301","DOI":"10.1007\/978-981-32-9949-8_22","article-title":"Role and challenges of unstructured big data in healthcare","volume":"Volume 1","author":"Adnan","year":"2020","journal-title":"Data Management, Analytics and Innovation: Proceedings of ICDMAI 2019"},{"key":"ref_98","doi-asserted-by":"crossref","unstructured":"Sedlakova, J., Daniore, P., Wintsch, A.H., Wolf, M., Stanikic, M., Haag, C., Sieber, C., Schneider, G., Staub, K., and Ettlin, D.A. (2023). Challenges and best practices for digital unstructured data enrichment in health research: A systematic narrative review. PLoS Digit. Health, 2.","DOI":"10.1371\/journal.pdig.0000347"},{"key":"ref_99","doi-asserted-by":"crossref","first-page":"514","DOI":"10.1136\/jamia.2010.003947","article-title":"Extracting medication information from clinical text","volume":"17","author":"Uzuner","year":"2010","journal-title":"J. Am. Med. Inform. Assoc."},{"key":"ref_100","doi-asserted-by":"crossref","first-page":"e54580","DOI":"10.2196\/54580","article-title":"An entity extraction pipeline for medical text records using large language models: Analytical study","volume":"26","author":"Wang","year":"2024","journal-title":"J. Med. Internet Res."},{"key":"ref_101","doi-asserted-by":"crossref","first-page":"275","DOI":"10.1590\/S0104-11692003000300002","article-title":"Contributions of qualitative research to evidence-based practice in nursing","volume":"11","author":"Ailinger","year":"2003","journal-title":"Rev. Lat.-Am. De Enferm."},{"key":"ref_102","doi-asserted-by":"crossref","first-page":"1005","DOI":"10.1177\/1049732303253523","article-title":"Data mining: Qualitative analysis with health informatics data","volume":"13","author":"Castellani","year":"2003","journal-title":"Qual. Health Res."},{"key":"ref_103","doi-asserted-by":"crossref","first-page":"1385","DOI":"10.5194\/essd-11-1385-2019","article-title":"The spatial allocation of population: A review of largescale gridded population data products and their fitness for use","volume":"11","author":"Leyk","year":"2019","journal-title":"Earth Syst. Sci. Data"},{"key":"ref_104","unstructured":"(2025, October 15). National Alzheimer\u2019s Coordinating Center (NACC) NACC Data and Resources. Available online: https:\/\/naccdata.org."},{"key":"ref_105","first-page":"250","article-title":"The national alzheimer\u2019s coordinating center (nacc) database: An alzheimer disease database","volume":"18","author":"Beekly","year":"2004","journal-title":"Alzheimer Dis. Assoc. Disord."},{"key":"ref_106","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1097\/WAD.0b013e318142774e","article-title":"The national alzheimer\u2019s coordinating center (nacc) database: The uniform data set","volume":"21","author":"Beekly","year":"2007","journal-title":"Alzheimer Dis. Assoc. Disord."},{"key":"ref_107","doi-asserted-by":"crossref","first-page":"351","DOI":"10.1097\/WAD.0000000000000279","article-title":"Version 3 of the national alzheimer\u2019s coordinating center\u2019s uniform data set","volume":"32","author":"Besser","year":"2018","journal-title":"Alzheimer Dis. Assoc. Disord."},{"key":"ref_108","doi-asserted-by":"crossref","first-page":"210","DOI":"10.1097\/01.wad.0000213865.09806.92","article-title":"The uniform data set (uds): Clinical and cognitive variables and descriptive data from alzheimer disease centers","volume":"20","author":"Morris","year":"2006","journal-title":"Alzheimer Dis. Assoc. Disord."},{"key":"ref_109","unstructured":"(2025, October 15). National Alzheimer\u2019s Coordinating Center (NACC) Uniform Data Set (UDS) Forms and Documentation. Available online: https:\/\/naccdata.org\/data-collection\/forms-documentation\/uds-3."},{"key":"ref_110","first-page":"e12450","article-title":"Representativeness of samples enrolled in alzheimer\u2019s disease research centers","volume":"15","author":"Renter","year":"2023","journal-title":"Alzheimer\u2019s Dement. Diagn. Assess. Dis. Monit."},{"key":"ref_111","doi-asserted-by":"crossref","first-page":"1791","DOI":"10.1212\/WNL.58.12.1791","article-title":"Phases of a\u03b2-deposition in the human brain and its relevance for the development of ad","volume":"58","author":"Thal","year":"2002","journal-title":"Neurology"},{"key":"ref_112","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1007\/BF00308809","article-title":"Neuropathological stageing of alzheimer-related changes","volume":"82","author":"Braak","year":"1991","journal-title":"Acta Neu-Ropathologica"},{"key":"ref_113","doi-asserted-by":"crossref","first-page":"479","DOI":"10.1212\/WNL.41.4.479","article-title":"The consortium to establish a registry for alzheimer\u2019s disease (cerad) part ii. Standardization of the neuropathologic assessment of alzheimer\u2019s disease","volume":"41","author":"Mirra","year":"1991","journal-title":"Neurology"},{"key":"ref_114","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s00401-011-0910-3","article-title":"National institute on aging\u2013alzheimer\u2019s association guidelines for the neuropathologic assessment of alzheimer\u2019s disease: A practical approach","volume":"123","author":"Montine","year":"2012","journal-title":"Acta Neuropathol."},{"key":"ref_115","doi-asserted-by":"crossref","first-page":"e079015","DOI":"10.1002\/alz.079015","article-title":"Harmonization of amyloid and tau pet tracers in a multi-center cohort from nia alzheimer\u2019s disease research centers in the united states","volume":"19","author":"Harrison","year":"2023","journal-title":"Alzheimer\u2019s Dement."},{"key":"ref_116","doi-asserted-by":"crossref","unstructured":"Dinov, I., Lozev, K., Petrosyan, P., Liu, Z., Eggert, P., Pierce, J., Zamanyan, A., Chakrapani, S., Van Horn, J., and Parker, D.S. (2010). Neuroimaging study designs, computational analyses and data provenance using the loni pipeline. PLoS ONE, 5.","DOI":"10.1371\/journal.pone.0013070"},{"key":"ref_117","doi-asserted-by":"crossref","first-page":"e080049","DOI":"10.1002\/alz.080049","article-title":"Relationships between neuroimaging parameters, apoe genotypes and composite scores for memory, executive functioning and language from the national alzheimer\u2019s coordinating center (nacc)","volume":"19","author":"Lee","year":"2023","journal-title":"Alzheimer\u2019s Dement."},{"key":"ref_118","doi-asserted-by":"crossref","unstructured":"Wang, R., Shen, C.-L., Sheng, V., Wu, H., Toler, Z., and Temesgen, S. (2021). A review of publicly patient-centered alzheimer\u2019s disease datasets. 2021 International Conference on Computational Science and Computational Intelligence (CSCI), IEEE.","DOI":"10.1109\/CSCI54926.2021.00247"},{"key":"ref_119","doi-asserted-by":"crossref","first-page":"2707","DOI":"10.1002\/hbm.25803","article-title":"The spectrum of data sharing policies in neuroimaging data repositories","volume":"43","author":"Jwa","year":"2022","journal-title":"Hum. Brain Mapp."},{"key":"ref_120","first-page":"111","article-title":"Open-source, rapid reporting of dementia evaluations","volume":"42","author":"Graves","year":"2015","journal-title":"J. Regist. Manag."},{"key":"ref_121","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1097\/WAD.0000000000000380","article-title":"The utility of the national alzheimer\u2019s coordinating center\u2019s database for the rapid assessment of evolving neuropathologic conditions","volume":"34","author":"Mock","year":"2020","journal-title":"Alzheimer Dis. Assoc. Disord."},{"key":"ref_122","doi-asserted-by":"crossref","unstructured":"Cuevas, E.P., Rodr, A., Palomo, V., Mart, A., and Mart\u00b4\u0131n-Requero, \u00c1. (2022). Tdp-43 pathology and prionic behavior in human cellular models of alzheimer\u2019s disease patients. Biomedicines, 10.","DOI":"10.3390\/biomedicines10020385"},{"key":"ref_123","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1097\/WAD.0000000000000380","article-title":"The utility of the national alzheimer\u2019s coordinating center\u2019s database for the rapid assess-ment of evolving neuropathologic conditions","volume":"34","author":"Gauthreaux","year":"2020","journal-title":"Alzheimer Dis. Assoc. Disord."},{"key":"ref_124","unstructured":"Project, O.A.I.B. (2025, October 15). Open Access Series of Imaging Studies (Oasis). Available online: https:\/\/www.oasis-brains.org."},{"key":"ref_125","doi-asserted-by":"crossref","first-page":"2677","DOI":"10.1162\/jocn.2009.21407","article-title":"Open access series of imaging studies: Longitudinal mri data in nondemented and demented older adults","volume":"22","author":"Marcus","year":"2010","journal-title":"J. Cogn. Neurosci."},{"key":"ref_126","doi-asserted-by":"crossref","first-page":"1498","DOI":"10.1162\/jocn.2007.19.9.1498","article-title":"Open ac-cess series of imaging studies (oasis): Cross-sectional mri data in young, middle aged, nondemented, and demented older adults","volume":"19","author":"Marcus","year":"2007","journal-title":"J. Cogn. Neurosci."},{"key":"ref_127","doi-asserted-by":"crossref","first-page":"160044","DOI":"10.1038\/sdata.2016.44","article-title":"The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments","volume":"3","author":"Gorgolewski","year":"2016","journal-title":"Sci. Data"},{"key":"ref_128","doi-asserted-by":"crossref","unstructured":"Frenzel, S., Wittfeld, K., Habes, M., Klinger-Koenig, J., Buelow, R., Voelzke, H., and Grabe, H.J. (2020). A biomarker for alzheimer\u2019s disease based on patterns of regional brain atrophy. Front. Psychiatry, 10.","DOI":"10.3389\/fpsyt.2019.00953"},{"key":"ref_129","doi-asserted-by":"crossref","first-page":"1427","DOI":"10.1016\/j.radi.2024.06.016","article-title":"Radiomics evaluation for the early detection of alzheimer\u2019s de-mentia using t1-weighted mri","volume":"30","author":"Jytzler","year":"2024","journal-title":"Radiography"},{"key":"ref_130","unstructured":"Ntampakis, N., Diamantaras, K., Chouvarda, I., Argyriou, V., and Sarigianndis, P. (2024). Enhanced deep learning methodologies and mri selection techniques for dementia diagnosis in the elderly popula-tion. arXiv."},{"key":"ref_131","doi-asserted-by":"crossref","first-page":"244","DOI":"10.35940\/ijeat.A1827.1010120","article-title":"Dementia prediction on oasis dataset using supervised and ensemble learning techniques","volume":"10","author":"Vinayak","year":"2020","journal-title":"Int. J. Eng. Adv. Technol."},{"key":"ref_132","doi-asserted-by":"crossref","unstructured":"LaMontagne, P.J., Benzinger, T.L., Morris, J.C., Keefe, S., Hornbeck, R., Xiong, C., Grant, E., Hassenstab, J., Moulder, K., and Vlassenko, A.G. (2019). Oasis-3: Longitudinal neuroimaging, clinical, and cognitive dataset for normal aging and alzheimer disease. medRxiv.","DOI":"10.1101\/2019.12.13.19014902"},{"key":"ref_133","doi-asserted-by":"crossref","unstructured":"Zhang, X.-Y., Zhang, H., Bao, Q.-N., Yin, Z.-H., Li, Y.-Q., Xia, M.-Z., Chen, Z.-H., Zhong, W.-Q., Wu, K.-X., and Yao, J. (2024). Diagnostic value of arterial spin labeling for alzheimer\u2019s disease: A systematic review and meta-analysis. PLoS ONE, 19.","DOI":"10.1371\/journal.pone.0311016"},{"key":"ref_134","doi-asserted-by":"crossref","first-page":"e230514","DOI":"10.1148\/ryai.230514","article-title":"Addressing the generalizability of ai in radiology using a novel data augmentation framework with synthetic patient image data: Proof-of-concept and external validation for classification tasks in multiple sclerosis","volume":"6","author":"Brugnara","year":"2024","journal-title":"Radiol. Artif. Intell."},{"key":"ref_135","doi-asserted-by":"crossref","unstructured":"Tanveer, M., Verma, S., Sharma, R., Goel, T., and Suganthan, P.N. (2023). Weighted kernel ridge regression based randomized network for alzheimer\u2019s disease diagnosis using susceptibility weighted images. 2023 International Joint Conference on Neural Networks (IJCNN), IEEE.","DOI":"10.1109\/IJCNN54540.2023.10191119"},{"key":"ref_136","doi-asserted-by":"crossref","first-page":"5210","DOI":"10.1038\/s41598-024-56001-9","article-title":"Automated detection of alzheimer\u2019s disease: A multi-modal approach with 3d mri and amyloid pet","volume":"14","author":"Castellano","year":"2024","journal-title":"Sci. Rep."},{"key":"ref_137","doi-asserted-by":"crossref","unstructured":"Mittal, K. (2023). Early stage detection of alzheimer\u2019s disease with microsoft azure based deep learning. Res. Sq., rs.3.rs-3352620.","DOI":"10.21203\/rs.3.rs-3352620\/v1"},{"key":"ref_138","doi-asserted-by":"crossref","unstructured":"Emen, A.\u00c7. (2024). Comparative Ordinal Longitudinal Data Analysis to Predict a Diagnosis of Alzheimer\u2019s Disease by Using Multimodal Data. [Master\u2019s Thesis, Middle East Technical University (Turkey)].","DOI":"10.22541\/au.176502433.39807870\/v1"},{"key":"ref_139","doi-asserted-by":"crossref","unstructured":"Chiumento, F., and Liu, M. (2024). Leveraging multimodal models for enhanced neuroimaging diagnostics in alzheimer\u2019s disease. 2024 IEEE International Conference on Big Data (BigData), IEEE.","DOI":"10.1109\/BigData62323.2024.10825149"},{"key":"ref_140","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1385\/NI:5:1:11","article-title":"The extensible neuroimaging archive toolkit: An informatics platform for managing, exploring, and sharing neuroimaging data","volume":"5","author":"Marcus","year":"2007","journal-title":"Neuroinformatics"},{"key":"ref_141","doi-asserted-by":"crossref","first-page":"254","DOI":"10.1016\/j.jneumeth.2015.05.024","article-title":"Amygdalar and hippocampal volume: A comparison between manual segmentation, freesurfer and vbm","volume":"253","author":"Grimm","year":"2015","journal-title":"J. Neurosci. Methods"},{"key":"ref_142","doi-asserted-by":"crossref","first-page":"166","DOI":"10.2463\/mrms.mp.2019-0199","article-title":"A robust and accurate deep-learning-based method for the segmentation of subcortical brain: Cross-dataset evaluation of generalization performance","volume":"20","author":"Furuhashi","year":"2021","journal-title":"Magn. Reson. Med. Sci."},{"key":"ref_143","unstructured":"(2025, October 15). Alzheimer\u2019s Disease Neuroimaging Initiative (ADNI) Adni: Alzheimer\u2019s Disease Neuroimaging Initiative. Available online: https:\/\/adni.loni.usc.edu."},{"key":"ref_144","first-page":"685","article-title":"The alzheimer\u2019s disease neuroimaging initiative (adni): Mri methods","volume":"27","author":"Jack","year":"2008","journal-title":"J. Magn. Reson. Imaging Off. J. Inter-Natl. Soc. Magn. Reson. Med."},{"key":"ref_145","doi-asserted-by":"crossref","first-page":"869","DOI":"10.1016\/j.nic.2005.09.008","article-title":"The alzheimer\u2019s disease neuroimaging initiative","volume":"15","author":"Mueller","year":"2005","journal-title":"Neuroimaging Clin."},{"key":"ref_146","doi-asserted-by":"crossref","first-page":"860","DOI":"10.1016\/j.jalz.2015.05.006","article-title":"The impact of the alzheimer\u2019s disease neuroimaging initiative 2: What role do public-private partnerships have in pushing the boundaries of clinical and basic science research on alzheimer\u2019s disease?","volume":"11","author":"Buckholtz","year":"2015","journal-title":"Alzheimer\u2019s Dement."},{"key":"ref_147","doi-asserted-by":"crossref","first-page":"8188","DOI":"10.1002\/alz.14256","article-title":"The origins of adni","volume":"20","author":"Potter","year":"2024","journal-title":"Alzheimer\u2019s Dement."},{"key":"ref_148","doi-asserted-by":"crossref","first-page":"275","DOI":"10.1016\/j.neuroimage.2014.03.036","article-title":"Alzheimer\u2019s Disease Neuroimaging Initiative (ADNI). Manifold population modeling as a neuro-imaging biomarker: Application to adni and adni-go","volume":"94","author":"Guerrero","year":"2014","journal-title":"NeuroImage"},{"key":"ref_149","doi-asserted-by":"crossref","first-page":"531","DOI":"10.1002\/gps.4179","article-title":"A voxel-based morphometry comparison of the 3.0 t adni-1 and adni-2 volumetric mri protocols","volume":"30","author":"Brunton","year":"2015","journal-title":"Interna-Tional J. Geriatr. Psychiatry"},{"key":"ref_150","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1016\/j.jalz.2010.03.006","article-title":"Clinical core of the alzheimer\u2019s disease neuroimaging initiative: Progress and plans","volume":"6","author":"Aisen","year":"2010","journal-title":"Alzheimer\u2019s Dement."},{"key":"ref_151","doi-asserted-by":"crossref","first-page":"e12226","DOI":"10.1002\/trc2.12226","article-title":"The worldwide alzheimer\u2019s disease neuroimaging initia-tive: Adni-3 updates and global perspectives","volume":"7","author":"Weber","year":"2021","journal-title":"Alzheimer\u2019s Dement. Transl. Res. Clin. Interv."},{"key":"ref_152","doi-asserted-by":"crossref","first-page":"2193","DOI":"10.1118\/1.3116776","article-title":"Measurement of mri scanner performance with the adni phantom","volume":"36","author":"Gunter","year":"2009","journal-title":"Med. Phys."},{"key":"ref_153","unstructured":"(2025, October 15). Alzheimer\u2019s Disease Neuroimaging Initiative (ADNI) ADNI Mri Data. Available online: https:\/\/adni.loni.usc.edu\/data-samples\/adni-data\/neuroimaging\/mri\/."},{"key":"ref_154","doi-asserted-by":"crossref","first-page":"9004","DOI":"10.1002\/alz.14311","article-title":"The adni administrative core: Ensuring adni\u2019s success and informing future ad clinical trials","volume":"20","author":"Nosheny","year":"2024","journal-title":"Alzheimer\u2019s Dement."},{"key":"ref_155","doi-asserted-by":"crossref","first-page":"8279","DOI":"10.1002\/alz.14242","article-title":"The alzheimer\u2019s disease neuroimaging initiative-4 (adni-4) engagement core: A culturally informed, community-engaged research (ci-cer) model to advance brain health equity","volume":"20","author":"Mindt","year":"2024","journal-title":"Alzheimer\u2019s Dement."},{"key":"ref_156","doi-asserted-by":"crossref","first-page":"2603","DOI":"10.1002\/alz.12640","article-title":"Screening and enrollment of underrepresented eth-nocultural and educational populations in the alzheimer\u2019s disease neuroimaging initiative (adni)","volume":"18","author":"Ashford","year":"2022","journal-title":"Alzheimer\u2019s Dement."},{"key":"ref_157","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1007\/s40615-022-01499-w","article-title":"Intersectional effects of race and educational attainment on memory function of middle-aged and older adults with alzheimer\u2019s disease","volume":"11","author":"Rahmani","year":"2024","journal-title":"J. Racial Ethn. Health Disparities"},{"key":"ref_158","unstructured":"(2025, October 15). Alzheimer\u2019s Disease Neuroimaging Initiative (ADNI) ADNI Frequently Asked Questions. Available online: https:\/\/adni.loni.usc.edu\/about\/faq\/."},{"key":"ref_159","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1186\/s13195-019-0539-3","article-title":"Participant and study partner pre-diction and identification of cognitive impairment in preclinical alzheimer\u2019s disease: Study partner vs. participant accuracy","volume":"11","author":"Ryan","year":"2019","journal-title":"Alzheimer\u2019s Res. Ther."},{"key":"ref_160","doi-asserted-by":"crossref","first-page":"1285","DOI":"10.1055\/a-1460-8566","article-title":"Metallic implants in mri\u2013hazards and imag-ing artifacts","volume":"Volume 193","author":"Peschke","year":"2021","journal-title":"R\u00f6Fo-Fortschritte auf dem Gebiet der R\u00f6ntgenstrahlen und der bildgebenden Verfahren"},{"key":"ref_161","doi-asserted-by":"crossref","first-page":"699","DOI":"10.1016\/j.jalz.2018.12.009","article-title":"Early versus late mci: Improved mci staging using a neuropsychological approach","volume":"15","author":"Edmonds","year":"2019","journal-title":"Alzheimer\u2019s Dement."},{"key":"ref_162","doi-asserted-by":"crossref","first-page":"7340","DOI":"10.1002\/alz.14165","article-title":"Alzheimer\u2019s Disease Neuroimaging Initiative. The adni pet core at 20","volume":"20","author":"Jagust","year":"2024","journal-title":"Alzheimer\u2019s Dement."},{"key":"ref_163","doi-asserted-by":"crossref","first-page":"1398","DOI":"10.1007\/s00259-014-2753-3","article-title":"Amyloid pet imaging in alzheimer\u2019s disease: A comparison of three radiotracers","volume":"41","author":"Landau","year":"2014","journal-title":"Eur. J. Nucl. Med. Mol. Imaging"},{"key":"ref_164","doi-asserted-by":"crossref","first-page":"590","DOI":"10.1097\/00004728-198308000-00003","article-title":"Regional cerebral metabolic alterations in dementia of the alzheimer type: Positron emission tomography with [18: 18:] fluorodeoxyglucose","volume":"7","author":"Friedland","year":"1983","journal-title":"J. Comput. Assist. Tomogr."},{"key":"ref_165","unstructured":"Center, P.C.C., and Core, N.B. (2025, October 15). Alzheimer\u2019s Disease Neuroimaging Initiative 2 (adni2) Protocol (adc-039). Citeseer 2014. Available online: https:\/\/adni.loni.usc.edu\/wp-content\/themes\/freshnews-dev-v2\/documents\/clinical\/ADNI-2_Protocol.pdf."},{"key":"ref_166","doi-asserted-by":"crossref","first-page":"e08827","DOI":"10.1016\/j.heliyon.2022.e08827","article-title":"Connectivity and variability of related cognitive subregions lead to different stages of progression toward alzheimer\u2019s disease","volume":"8","author":"Sheng","year":"2022","journal-title":"Heliyon"},{"key":"ref_167","unstructured":"(2025, October 15). Alzheimer\u2019s Disease Neuroimaging Initiative (ADNI) ADNI Clinical Data. Available online: https:\/\/adni.loni.usc.edu\/."},{"key":"ref_168","doi-asserted-by":"crossref","unstructured":"Trzepacz, P.T., Hochstetler, H., Wang, S., Walker, B., Saykin, A.J., and Initiative, A.D.N. (2015). Relation-ship between the montreal cognitive assessment and mini-mental state examination for assessment of mild cognitive impairment in older adults. BMC Geriatr., 15.","DOI":"10.1186\/s12877-015-0103-3"},{"key":"ref_169","doi-asserted-by":"crossref","first-page":"e14264","DOI":"10.1002\/alz.14264","article-title":"Adni biomarker core: A review of progress since 2004 and future challenges","volume":"21","author":"Shaw","year":"2025","journal-title":"Alzheimer\u2019s Dement."},{"key":"ref_170","doi-asserted-by":"crossref","first-page":"307","DOI":"10.1002\/alz.12797","article-title":"Increasing participant diversity in ad research: Plans for digital screening, blood testing, and a community-engaged approach in the alzheimer\u2019s disease neuroimaging initiative 4","volume":"19","author":"Weiner","year":"2023","journal-title":"Alzheimer\u2019s Dement."},{"key":"ref_171","doi-asserted-by":"crossref","first-page":"8048","DOI":"10.1002\/alz.14303","article-title":"Implementation and validation of face de-identification (de-facing) in adni4","volume":"20","author":"Schwarz","year":"2024","journal-title":"Alzheimer\u2019s Dement."},{"key":"ref_172","doi-asserted-by":"crossref","first-page":"332","DOI":"10.1016\/j.jalz.2012.06.004","article-title":"Standardization of analysis sets for reporting results from adni mri data","volume":"9","author":"Wyman","year":"2013","journal-title":"Alzheimer\u2019s Dement."},{"key":"ref_173","doi-asserted-by":"crossref","first-page":"7395","DOI":"10.1002\/alz.14129","article-title":"Data sharing as the foundation of discovery: Adni and break-throughs in alzheimer\u2019s disease","volume":"20","author":"Travaglia","year":"2024","journal-title":"Alzheimer\u2019s Dement."},{"key":"ref_174","doi-asserted-by":"crossref","unstructured":"Yang, Y., Bagyinszky, E., and An, S.S.A. (2023). Presenilin-1 (psen1) mutations: Clinical phenotypes beyond alzheimer\u2019s disease. Int. J. Mol. Sci., 24.","DOI":"10.3390\/ijms24098417"},{"key":"ref_175","doi-asserted-by":"crossref","first-page":"891","DOI":"10.1038\/sj.mp.4001101","article-title":"Regulatory region variability in the human presenilin-2 (psen2) gene: Potential contribution to the gene activity and risk for ad","volume":"7","author":"Riazanskaia","year":"2002","journal-title":"Mol. Psychiatry"},{"key":"ref_176","doi-asserted-by":"crossref","first-page":"1250","DOI":"10.1212\/WNL.0b013e3182515972","article-title":"Role of common and rare app dna sequence variants in alzheimer disease","volume":"78","author":"Hooli","year":"2012","journal-title":"Neurology"},{"key":"ref_177","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1093\/procel\/pwae026","article-title":"Alzheimer\u2019s disease: Insights into pathology, molecular mechanisms, and therapy","volume":"16","author":"Zheng","year":"2025","journal-title":"Protein Cell"},{"key":"ref_178","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1038\/s43586-021-00056-9","article-title":"Genome-wide association studies","volume":"1","author":"Uffelmann","year":"2021","journal-title":"Nat. Rev. Methods Primers"},{"key":"ref_179","doi-asserted-by":"crossref","first-page":"1373","DOI":"10.1038\/ng.3916","article-title":"Rare coding variants in plcg2, abi3, and trem2 implicate microglial-mediated innate immunity in alzheimer\u2019s disease","volume":"49","author":"Sims","year":"2017","journal-title":"Nat. Genet."},{"key":"ref_180","doi-asserted-by":"crossref","first-page":"414","DOI":"10.1038\/s41588-019-0358-2","article-title":"Genetic meta-analysis of diagnosed alzheimer\u2019s disease identifies new risk loci and implicates a\u03b2, tau, immunity and lipid processing","volume":"51","author":"Kunkle","year":"2019","journal-title":"Nat. Genet."},{"key":"ref_181","doi-asserted-by":"crossref","unstructured":"Freudenberg-Hua, Y., Li, W., and Davies, P. (2018). The role of genetics in advancing precision medicine for alzheimer\u2019s disease\u2014A narrative review. Front. Med., 5.","DOI":"10.3389\/fmed.2018.00108"},{"key":"ref_182","unstructured":"Biobank, U.K. (2025, October 15). Uk Biobank. Available online: https:\/\/www.ukbiobank.ac.uk."},{"key":"ref_183","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1038\/s41586-018-0579-z","article-title":"The uk biobank resource with deep phenotyping and genomic data","volume":"562","author":"Bycroft","year":"2018","journal-title":"Nature"},{"key":"ref_184","doi-asserted-by":"crossref","first-page":"435","DOI":"10.1089\/bio.2015.0030","article-title":"Biobank finances: A socio-economic analysis and review","volume":"13","author":"Gee","year":"2015","journal-title":"Biopreservation Biobanking"},{"key":"ref_185","doi-asserted-by":"crossref","first-page":"1026","DOI":"10.1093\/aje\/kwx246","article-title":"Comparison of sociodemographic and health-related characteristics of uk biobank partic-ipants with those of the general population","volume":"186","author":"Fry","year":"2017","journal-title":"Am. J. Epidemiol."},{"key":"ref_186","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1016\/j.hlpt.2012.07.003","article-title":"Uk biobank: Current status and what it means for epidemiology","volume":"1","author":"Allen","year":"2012","journal-title":"Health Policy Technol."},{"key":"ref_187","doi-asserted-by":"crossref","first-page":"234","DOI":"10.1093\/ije\/dym276","article-title":"The uk biobank sample handling and storage protocol for the col-lection, processing and archiving of human blood and urine","volume":"37","author":"Elliott","year":"2008","journal-title":"Int. J. Epidemiol."},{"key":"ref_188","doi-asserted-by":"crossref","first-page":"dyae054","DOI":"10.1093\/ije\/dyae054","article-title":"Reweighting uk biobank corrects for pervasive selection bias due to volunteering","volume":"53","author":"Domingue","year":"2024","journal-title":"Int. J. Epidemiol."},{"key":"ref_189","unstructured":"Biobank, U. (2007). Uk Biobank Ethics and Governance Framework, UK BIOBANK."},{"key":"ref_190","doi-asserted-by":"crossref","first-page":"385","DOI":"10.1080\/09581590500523533","article-title":"\u2018ethics was here\u2019: Studying the language-games of ethics in the case of uk biobank","volume":"15","author":"Hoeyer","year":"2005","journal-title":"Crit. Public Health"},{"key":"ref_191","doi-asserted-by":"crossref","first-page":"214","DOI":"10.1016\/j.clnesp.2024.06.040","article-title":"Comparison of body composition measures assessed by bioelectrical impedance analysis versus dual-energy x-ray ab-sorptiometry in the united kingdom biobank","volume":"63","author":"Feng","year":"2024","journal-title":"Clin. Nutr. ESPEN"},{"key":"ref_192","unstructured":"Biobank, U.K. (2025, October 15). Brain Mri Imaging Protocols and Data. Available online: https:\/\/www.ukbiobank.ac.uk\/."},{"key":"ref_193","doi-asserted-by":"crossref","first-page":"132687","DOI":"10.1016\/j.ijcard.2024.132687","article-title":"Left ventricular size and heart failure: A cardiac mri assessment of 38,129 individuals from the uk biobank","volume":"419","author":"Rowe","year":"2025","journal-title":"Int. J. Cardiol."},{"key":"ref_194","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1093\/ehjci\/jez279","article-title":"The impact of visceral and general obesity on vascular and left ventricular function and geometry: A cross-sectional magnetic resonance imaging study of the uk biobank","volume":"21","author":"Dekkers","year":"2020","journal-title":"Eur. Heart J.-Cardiovasc. Imaging"},{"key":"ref_195","doi-asserted-by":"crossref","first-page":"532","DOI":"10.1161\/HYPERTENSIONAHA.121.17403","article-title":"Blood pressure determinants of cerebral white matter hy-perintensities and microstructural injury: Uk biobank cohort study","volume":"78","author":"Wartolowska","year":"2021","journal-title":"Hypertension"},{"key":"ref_196","doi-asserted-by":"crossref","first-page":"n826","DOI":"10.1136\/bmj.n826","article-title":"Linked electronic health records for research on a nationwide cohort of more than 54 million people in england: Data resource","volume":"373","author":"Wood","year":"2021","journal-title":"BMJ"},{"key":"ref_197","doi-asserted-by":"crossref","first-page":"e274","DOI":"10.1016\/S2665-9913(21)00401-X","article-title":"Gout and the risk of covid-19 diagnosis and death in the uk biobank: A population-based study","volume":"4","author":"Topless","year":"2022","journal-title":"Lancet Rheumatol."},{"key":"ref_198","doi-asserted-by":"crossref","first-page":"990","DOI":"10.1038\/ng.939","article-title":"Large-scale genome-wide association studies in east asians identify new genetic loci influencing metabolic traits","volume":"43","author":"Kim","year":"2011","journal-title":"Nat. Genet."},{"key":"ref_199","doi-asserted-by":"crossref","first-page":"628","DOI":"10.1038\/s41586-021-04103-z","article-title":"Exome sequencing and analysis of 454,787 uk biobank participants","volume":"599","author":"Backman","year":"2021","journal-title":"Nature"},{"key":"ref_200","doi-asserted-by":"crossref","unstructured":"Li, S., Carss, K.J., Halldorsson, B.V., Cortes, A., and Consortium, U.B.W.-G.S. (2023). Whole-genome sequencing of half-a-million uk biobank participants. medRxiv.","DOI":"10.1101\/2023.12.06.23299426"},{"key":"ref_201","doi-asserted-by":"crossref","first-page":"7096","DOI":"10.1038\/s41467-023-42758-6","article-title":"Poor sleep and shift work associate with increased blood pressure and inflammation in uk biobank participants","volume":"14","author":"Kanki","year":"2023","journal-title":"Nat. Commun."},{"key":"ref_202","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1038\/s41588-020-00757-z","article-title":"Genetics of 35 blood and urine biomarkers in the uk biobank","volume":"53","author":"Tanigawa","year":"2021","journal-title":"Nat. Genet."},{"key":"ref_203","doi-asserted-by":"crossref","unstructured":"Doherty, A., Jackson, D., Hammerla, N., Pl, T., Olivier, P., Granat, M.H., White, T., Van Hees, V.T., Trenell, M.I., and Owen, C.G. (2017). Large scale population assessment of physical activity using wrist worn accelerometers: The uk biobank study. PLoS ONE, 12.","DOI":"10.1371\/journal.pone.0169649"},{"key":"ref_204","unstructured":"Biobank, U.K. (2025, October 15). Cognitive Function Data and Assessments. Available online: https:\/\/www.ukbiobank.ac.uk\/about-our-data\/."},{"key":"ref_205","doi-asserted-by":"crossref","unstructured":"Bradley, V., and Nichols, T.E. (2022). Addressing selection bias in the uk biobank neurological imaging cohort. medRxiv.","DOI":"10.1101\/2022.01.13.22269266"},{"key":"ref_206","unstructured":"(2025, October 15). NCBI dbGaP Database of Genotypes and Phenotypes (dbgap), Available online: https:\/\/www.ncbi.nlm.nih.gov\/gap\/."},{"key":"ref_207","doi-asserted-by":"crossref","first-page":"D975","DOI":"10.1093\/nar\/gkt1211","article-title":"Ncbi\u2019s database of genotypes and phenotypes: Dbgap","volume":"42","author":"Tryka","year":"2014","journal-title":"Nucleic Acids Res."},{"key":"ref_208","doi-asserted-by":"crossref","unstructured":"Reyes, V.P., Kitony, J.K., Nishiuchi, S., Makihara, D., and Doi, K. (2022). Utilization of genotyping-by-sequencing (gbs) for rice pre-breeding and improvement: A review. Life, 12.","DOI":"10.3390\/life12111752"},{"key":"ref_209","unstructured":"Tryka, K.A., Hao, L., Sturcke, A., Jin, Y., Kimura, M., Wang, Z.Y., Ziyabari, L., Lee, M., and Feolo, M. (2024, September 04). The database of genotypes and phenotypes (dbgap) and phegeni, The NCBI Handbook [Internet], Available online: https:\/\/www.ncbi.nlm.nih.gov\/books\/NBK154410\/."},{"key":"ref_210","doi-asserted-by":"crossref","first-page":"113cm34","DOI":"10.1126\/scitranslmed.3002788","article-title":"dbgap data access requests: A call for greater transparency","volume":"3","author":"Walker","year":"2011","journal-title":"Sci. Transl. Med."},{"key":"ref_211","doi-asserted-by":"crossref","unstructured":"(2014). Data Use Under the nih gwas Data Sharing Policy and Future Directions. Nat. Genet., 46, 934\u2013938.","DOI":"10.1038\/ng.3062"},{"key":"ref_212","doi-asserted-by":"crossref","first-page":"D819","DOI":"10.1093\/nar\/gkw1139","article-title":"The dbgap data browser: A new tool for browsing dbgap controlled-access genomic data","volume":"45","author":"Wong","year":"2017","journal-title":"Nucleic Acids Res."},{"key":"ref_213","unstructured":"(2025, October 15). NIAGADS: NIA Genetics of Alzheimer\u2019s Disease Data Storage Site Niagads. Available online: https:\/\/www.niagads.org."},{"key":"ref_214","doi-asserted-by":"crossref","unstructured":"Kuzma, A., Valladares, O., Greenfest-Allen, E., Nicaretta, H., Kirsch, M., Ren, Y., Katanic, Z., White, H., Wilk, A., and Bass, L. (2024). Niagads: A comprehensive national data repository for alzheimer\u2019s disease and related dementia genetics and genomics research. medRxiv.","DOI":"10.1101\/2024.10.07.24315029"},{"key":"ref_215","doi-asserted-by":"crossref","first-page":"1123","DOI":"10.1002\/alz.13509","article-title":"Niagads alzheimer\u2019s genomicsdb: A resource for exploring alzheimer\u2019s disease genetic and genomic knowledge","volume":"20","author":"Valladares","year":"2024","journal-title":"Alzheimer\u2019s Dement."},{"key":"ref_216","doi-asserted-by":"crossref","first-page":"e70255","DOI":"10.1002\/alz.70255","article-title":"Niagads: A data repository for alzheimer\u2019s disease and related dementia genomics","volume":"21","author":"Kuzma","year":"2025","journal-title":"Alzheimer\u2019s Dement."},{"key":"ref_217","doi-asserted-by":"crossref","first-page":"223","DOI":"10.1093\/bfgp\/elab024","article-title":"Review of multi-omics data resources and integrative analysis for human brain disorders","volume":"20","author":"Dong","year":"2021","journal-title":"Brief. Funct. Genom."},{"key":"ref_218","doi-asserted-by":"crossref","first-page":"1177","DOI":"10.1093\/clinchem\/hvac113","article-title":"Clinical validation of genome reference consor-tium human build 38 in a laboratory utilizing next-generation sequencing technologies","volume":"68","author":"Lansdon","year":"2022","journal-title":"Clin. Chem."},{"key":"ref_219","doi-asserted-by":"crossref","first-page":"e200012","DOI":"10.1212\/NXG.0000000000200012","article-title":"A fast and robust strategy to remove variant-level artifacts in alzheimer disease sequencing project data","volume":"8","author":"Belloy","year":"2022","journal-title":"Neurol. Genet."},{"key":"ref_220","doi-asserted-by":"crossref","first-page":"e062285","DOI":"10.1002\/alz.062285","article-title":"Nia genetics of alzheimer\u2019s disease data storage site (niagads): 2022 update","volume":"18","author":"Issen","year":"2022","journal-title":"Alzheimer\u2019s Dement."},{"key":"ref_221","doi-asserted-by":"crossref","first-page":"975795","DOI":"10.3389\/fnins.2022.975795","article-title":"Restricted access data in the neurosciences: Are the restrictions always justified?","volume":"16","author":"Lathe","year":"2023","journal-title":"Front. Neurosci."},{"key":"ref_222","doi-asserted-by":"crossref","first-page":"e70237","DOI":"10.1002\/alz.70237","article-title":"Alzheimer\u2019s disease sequencing project release 4 whole genome sequencing dataset","volume":"21","author":"Leung","year":"2025","journal-title":"Alzheimer\u2019s Dement."},{"key":"ref_223","doi-asserted-by":"crossref","first-page":"e12462","DOI":"10.1002\/trc2.12462","article-title":"The power of representation: Statistical analysis of diversity in us alzheimer\u2019s disease genetics data","volume":"10","author":"Xue","year":"2024","journal-title":"Alzheimer\u2019s Dement. Transl. Res. Clin. Interv."},{"key":"ref_224","doi-asserted-by":"crossref","first-page":"e089040","DOI":"10.1002\/alz.089040","article-title":"Nia genetics of alzheimer\u2019s disease data storage site (niagads)","volume":"20","author":"Saravanan","year":"2025","journal-title":"Alzheimer\u2019s Dement."},{"key":"ref_225","doi-asserted-by":"crossref","first-page":"e080414","DOI":"10.1002\/alz.080414","article-title":"Easing the burden: Ad trials sharing data and samples through a national repository","volume":"19","author":"Foroud","year":"2023","journal-title":"Alzheimer\u2019s Dement."},{"key":"ref_226","doi-asserted-by":"crossref","first-page":"e12095","DOI":"10.1002\/trc2.12095","article-title":"Targeting abnormal metabolism in alzheimer\u2019s disease: The drug repurposing for effective alzheimer\u2019s medicines (dream) study","volume":"6","author":"Desai","year":"2020","journal-title":"Alzheimer\u2019s Dement. Transl. Res. Clin. Interv."},{"key":"ref_227","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1007\/s12015-017-9738-0","article-title":"Microphysiological human brain and neural systems-on-a-chip: Potential alternatives to small animal models and emerging platforms for drug discovery and personalized medicine","volume":"13","author":"Haring","year":"2017","journal-title":"Stem Cell Rev. Rep."},{"key":"ref_228","unstructured":"(2025, October 15). EMBL-EBI Chembl Database. Available online: https:\/\/www.ebi.ac.uk\/chembl\/."},{"key":"ref_229","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13321-025-00963-z","article-title":"Fifteen years of chembl and its role in cheminformatics and drug discovery","volume":"17","author":"Zdrazil","year":"2025","journal-title":"J. Cheminform."},{"key":"ref_230","doi-asserted-by":"crossref","first-page":"D1083","DOI":"10.1093\/nar\/gkt1031","article-title":"The chembl bioactivity database: An update","volume":"42","author":"Bento","year":"2014","journal-title":"Nucleic Acids Res."},{"key":"ref_231","doi-asserted-by":"crossref","first-page":"D1100","DOI":"10.1093\/nar\/gkr777","article-title":"Chembl: A large-scale bioactivity database for drug discovery","volume":"40","author":"Gaulton","year":"2012","journal-title":"Nucleic Acids Res."},{"key":"ref_232","doi-asserted-by":"crossref","first-page":"ELIXIR\u2013278","DOI":"10.12688\/f1000research.20559.1","article-title":"A community proposal to integrate structural bioinformatics activities in elixir (3d-bioinfo community)","volume":"9","author":"Orengo","year":"2020","journal-title":"F1000Research"},{"key":"ref_233","doi-asserted-by":"crossref","first-page":"885","DOI":"10.1007\/s10822-015-9860-5","article-title":"Activity, assay and target data curation and quality in the chembl database","volume":"29","author":"Papadatos","year":"2015","journal-title":"J. Comput.-Aided Mol. Des."},{"key":"ref_234","doi-asserted-by":"crossref","first-page":"W612","DOI":"10.1093\/nar\/gkv352","article-title":"Chembl web services: Streamlining access to drug discovery data and utilities","volume":"43","author":"Davies","year":"2015","journal-title":"Nucleic Acids Res."},{"key":"ref_235","doi-asserted-by":"crossref","first-page":"D1180","DOI":"10.1093\/nar\/gkad1004","article-title":"The ChEMBL database in 2023: A drug discovery platform spanning multiple bioactivity data types and time periods","volume":"52","author":"Zdrazil","year":"2024","journal-title":"Nucleic Acids Res."},{"key":"ref_236","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1186\/1758-2946-5-23","article-title":"The chembl database as linked open data","volume":"5","author":"Willighagen","year":"2013","journal-title":"J. Cheminform."},{"key":"ref_237","first-page":"28","article-title":"A web-based multi-fingerprint target prediction tool using ChEMBL bioactivity data","volume":"7","author":"Awale","year":"2015","journal-title":"J. Cheminform."},{"key":"ref_238","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1186\/s13326-016-0102-0","article-title":"A drug target slim: Using gene ontology and gene ontology annotations to navigate protein-ligand target space in ChEMBL","volume":"7","author":"Mutowo","year":"2016","journal-title":"J. Biomed. Semant."},{"key":"ref_239","unstructured":"Campos, D., and Ji, H. (2021). IMG2SMI: Translating molecular structure images to simplified molecular-input line-entry system. arXiv."},{"key":"ref_240","doi-asserted-by":"crossref","first-page":"881","DOI":"10.1002\/minf.201300103","article-title":"Comparing the chemical structure and protein content of ChEMBL, DrugBank, Human Metabolome Database and the Therapeutic Target Database","volume":"32","author":"Southan","year":"2013","journal-title":"Mol. Inform."},{"key":"ref_241","first-page":"757","article-title":"Using ChEMBL web services for building applications and data processing workflows relevant to drug discovery","volume":"12","author":"Nowotka","year":"2017","journal-title":"Expert Opin. Drug Discov."},{"key":"ref_242","doi-asserted-by":"crossref","first-page":"D930","DOI":"10.1093\/nar\/gky1075","article-title":"ChEMBL: Towards direct deposition of bioassay data","volume":"47","author":"Mendez","year":"2019","journal-title":"Nucleic Acids Res."},{"key":"ref_243","doi-asserted-by":"crossref","first-page":"108159","DOI":"10.1016\/j.dib.2022.108159","article-title":"A new chembl dataset for the similarity-based target fishing engine fasttargetpred: Annotation of an exhaustive list of linear tetrapeptides","volume":"42","author":"Tanwar","year":"2022","journal-title":"Data Brief"},{"key":"ref_244","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1186\/s13321-019-0351-x","article-title":"TeachOpenCADD: A teaching platform for computer-aided drug design using open source packages and data","volume":"11","author":"Sydow","year":"2019","journal-title":"J. Cheminform."},{"key":"ref_245","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1186\/s13321-021-00515-1","article-title":"IDSM ChemWebRDF: SPARQLing small-molecule datasets","volume":"13","author":"Galgonek","year":"2021","journal-title":"J. Cheminform."},{"key":"ref_246","doi-asserted-by":"crossref","first-page":"4476","DOI":"10.1021\/acschemneuro.9b00302","article-title":"Big data challenges targeting proteins in GPCR signaling pathways; combining PTML-ChEMBL models and [35S] GTP\u03b3S binding assays","volume":"10","year":"2019","journal-title":"ACS Chem. Neurosci."},{"key":"ref_247","doi-asserted-by":"crossref","first-page":"1695","DOI":"10.1093\/bioinformatics\/btv010","article-title":"ADME SARfari: Comparative genomics of drug metabolizing systems","volume":"31","author":"Davies","year":"2015","journal-title":"Bioinformatics"},{"key":"ref_248","doi-asserted-by":"crossref","first-page":"298","DOI":"10.1093\/bioinformatics\/btt666","article-title":"myChEMBL: A virtual machine implementation of open data and chemin-formatics tools","volume":"30","author":"Ochoa","year":"2014","journal-title":"Bioinformatics"},{"key":"ref_249","doi-asserted-by":"crossref","first-page":"D1220","DOI":"10.1093\/nar\/gkv1253","article-title":"SureChEMBL: A large-scale, chemically annotated patent document database","volume":"44","author":"Papadatos","year":"2016","journal-title":"Nucleic Acids Res."},{"key":"ref_250","unstructured":"(2025, October 15). DrugBank Drugbank Online. Available online: https:\/\/go.drugbank.com."},{"key":"ref_251","doi-asserted-by":"crossref","first-page":"D901","DOI":"10.1093\/nar\/gkm958","article-title":"DrugBank: A knowledgebase for drugs, drug actions and drug targets","volume":"36","author":"Wishart","year":"2008","journal-title":"Nucleic Acids Res."},{"key":"ref_252","doi-asserted-by":"crossref","first-page":"D1074","DOI":"10.1093\/nar\/gkx1037","article-title":"DrugBank 5.0: A major update to the DrugBank database for 2018","volume":"46","author":"Wishart","year":"2018","journal-title":"Nucleic Acids Res."},{"key":"ref_253","doi-asserted-by":"crossref","first-page":"D1035","DOI":"10.1093\/nar\/gkq1126","article-title":"DrugBank 3.0: A comprehensive resource for \u2018omics\u2019 research on drugs","volume":"39","author":"Knox","year":"2010","journal-title":"Nucleic Acids Res."},{"key":"ref_254","doi-asserted-by":"crossref","first-page":"1375","DOI":"10.2174\/1389557520666200429102334","article-title":"Applications of quantitative structure-activity relationships (QSAR) based vir-tual screening in drug design: A review","volume":"20","author":"Achary","year":"2020","journal-title":"Mini Rev. Med. Chem."},{"key":"ref_255","doi-asserted-by":"crossref","first-page":"2958","DOI":"10.1021\/jm501873p","article-title":"Ensemble-based virtual screening for cannabinoid-like potentiators of the human glycine receptor \u03b11 for the treatment of pain","volume":"58","author":"Wells","year":"2015","journal-title":"J. Med. Chem."},{"key":"ref_256","doi-asserted-by":"crossref","first-page":"D1265","DOI":"10.1093\/nar\/gkad976","article-title":"DrugBank 6.0: The DrugBank knowledgebase for 2024","volume":"52","author":"Knox","year":"2024","journal-title":"Nucleic Acids Res."},{"key":"ref_257","doi-asserted-by":"crossref","unstructured":"Peng, Q., and Ren, Y. (2024). A hybrid neural network model with entity-related knowledge for ad-verse drug reaction detection. 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE.","DOI":"10.1109\/BIBM62325.2024.10822078"},{"key":"ref_258","doi-asserted-by":"crossref","unstructured":"Bilokon, P., Bilokon, O., and Amen, S. (2023). A compendium of data sources for data science, machine learning, and artificial intelligence. arXiv.","DOI":"10.2139\/ssrn.4567555"},{"key":"ref_259","first-page":"1","article-title":"DrugBank as a recognized bank of drugs having ATM card in database","volume":"3","author":"Sen","year":"2015","journal-title":"World J. Pharm. Sci."},{"key":"ref_260","doi-asserted-by":"crossref","first-page":"105740","DOI":"10.1016\/j.antiviral.2023.105740","article-title":"Artificial intelligence: Machine learning approach for screening large database and drug discovery","volume":"220","author":"Parvatikar","year":"2023","journal-title":"Antivir. Res."},{"key":"ref_261","doi-asserted-by":"crossref","first-page":"D668","DOI":"10.1093\/nar\/gkj067","article-title":"DrugBank: A comprehensive resource for in silico drug discovery and exploration","volume":"34","author":"Wishart","year":"2006","journal-title":"Nucleic Acids Res."},{"key":"ref_262","doi-asserted-by":"crossref","unstructured":"Rietveld, L., Beek, W., and Schlobach, S. (2015). LOD lab: Experiments at LOD scale. International Semantic Web Conference, Springer International Publishing.","DOI":"10.1007\/978-3-319-25010-6_23"},{"key":"ref_263","first-page":"519","article-title":"Machine learning of neuroimaging for assisted diagnosis of cognitive impairment and dementia: A systematic review","volume":"10","author":"Pellegrini","year":"2018","journal-title":"Alzheimer\u2019s Dement. Diagn. Assess. Dis. Monit."},{"key":"ref_264","doi-asserted-by":"crossref","first-page":"844","DOI":"10.1016\/j.jalz.2014.01.001","article-title":"A conceptual framework for research on subjective cognitive decline in preclinical alzheimer\u2019s disease","volume":"10","author":"Jessen","year":"2014","journal-title":"Alzheimer\u2019s Dement."},{"key":"ref_265","doi-asserted-by":"crossref","unstructured":"Sahay, T., Aggarwal, A., Bansal, A., and Chandra, M. (2015). Svm and ann: A comparative evaluation. 2015 1st International Conference on Next Generation Computing Technologies (NGCT), IEEE.","DOI":"10.1109\/NGCT.2015.7375263"},{"key":"ref_266","doi-asserted-by":"crossref","first-page":"3297","DOI":"10.4236\/am.2014.521307","article-title":"Three-objective programming with continuous variable genetic algorithm","volume":"5","author":"Fita","year":"2014","journal-title":"Appl. Math."},{"key":"ref_267","doi-asserted-by":"crossref","first-page":"683","DOI":"10.1517\/17460441.2011.579100","article-title":"Bindingdb and chembl: Online compound databases for drug discovery","volume":"6","author":"Wassermann","year":"2011","journal-title":"Expert Opin. Drug Discov."},{"key":"ref_268","doi-asserted-by":"crossref","first-page":"1239","DOI":"10.1002\/alz.13525","article-title":"A novel ultrasensitive assay for plasma p-tau217: Performance in individuals with subjective cognitive decline and early alzheimer\u2019s disease","volume":"20","author":"Ferreira","year":"2024","journal-title":"Alzheimer\u2019s Dement."},{"key":"ref_269","doi-asserted-by":"crossref","unstructured":"Stavropoulos, T.G., Lazarou, I., Diaz, A., Gove, D., Georges, J., Manyakov, N.V., Pich, E.M., Hinds, C., Tsolaki, M., and Nikolopoulos, S. (2021). Wearable devices for assessing function in alzheimer\u2019s disease: A european public involvement activity about the features and preferences of patients and caregivers. Front. Aging Neurosci., 13.","DOI":"10.3389\/fnagi.2021.643135"},{"key":"ref_270","doi-asserted-by":"crossref","first-page":"465","DOI":"10.1038\/s41582-020-0373-z","article-title":"Crispr-based functional genomics for neurological disease","volume":"16","author":"Kampmann","year":"2020","journal-title":"Nat. Rev. Neurol."}],"container-title":["Bioengineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2306-5354\/13\/6\/698\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,20]],"date-time":"2026-06-20T04:16:09Z","timestamp":1781928969000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2306-5354\/13\/6\/698"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,18]]},"references-count":270,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2026,6]]}},"alternative-id":["bioengineering13060698"],"URL":"https:\/\/doi.org\/10.3390\/bioengineering13060698","relation":{},"ISSN":["2306-5354"],"issn-type":[{"value":"2306-5354","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,6,18]]}}}