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Elsevier Inc."},{"key":"ref43","doi-asserted-by":"crossref","unstructured":"Chopra S, Hadsell R, LeCun Y. Learning a similarity metric discriminatively, with application to face verification. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 2005. pp. 539\u2013546.","DOI":"10.1109\/CVPR.2005.202"},{"key":"ref44","unstructured":"Koch G, Zemel\u2014ICML Deep Learning \u2026 R, 2015. Siamese neural networks for one-shot image recognition. pdfs.semanticscholar.org. 2015; <ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/pdfs.semanticscholar.org\/e669\/55e4a24b611c54f9e7f6b178e7cbaddd0fbb.pdf\" xlink:type=\"simple\">https:\/\/pdfs.semanticscholar.org\/e669\/55e4a24b611c54f9e7f6b178e7cbaddd0fbb.pdf<\/ext-link>"},{"key":"ref45","unstructured":"Prince M, Bryce R, Ferri C. World Alzheimer Report 2011: The benefits of early diagnosis and intervention. Alzheimer\u2019s Disease International; 2011."},{"key":"ref46","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1186\/1471-2377-14-101","article-title":"Early intervention in Alzheimer\u2019s disease: a health economic study of the effects of diagnostic timing","volume":"14","author":"JH Barnett","year":"2014","journal-title":"BMC Neurol"},{"key":"ref47","article-title":"Clinical trials and late\u2010stage drug development for Alzheimer\u2019s disease: an appraisal from 1984 to 2014","author":"LS Schneider","year":"2014","journal-title":"J Intern Med. Wiley Online Library"},{"key":"ref48","doi-asserted-by":"crossref","first-page":"1429","DOI":"10.1016\/j.neurobiolaging.2010.04.022","article-title":"Boosting power for clinical trials using classifiers based on multiple biomarkers","volume":"31","author":"O Kohannim","year":"2010","journal-title":"Neurobiol Aging"},{"key":"ref49","doi-asserted-by":"crossref","first-page":"39880","DOI":"10.1038\/srep39880","article-title":"Longitudinal measurement and hierarchical classification framework for the prediction of Alzheimer\u2019s disease","volume":"7","author":"M Huang","year":"2017","journal-title":"Sci Rep"}],"updated-by":[{"DOI":"10.1371\/journal.pcbi.1006376","type":"new_version","label":"New version","source":"publisher","updated":{"date-parts":[[2018,9,26]],"date-time":"2018-09-26T00:00:00Z","timestamp":1537920000000}}],"container-title":["PLOS Computational Biology"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/dx.plos.org\/10.1371\/journal.pcbi.1006376","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,7]],"date-time":"2025-07-07T05:55:02Z","timestamp":1751867702000},"score":1,"resource":{"primary":{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pcbi.1006376"}},"subtitle":[],"editor":[{"given":"Saad","family":"Jbabdi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"editor"}]}],"short-title":[],"issued":{"date-parts":[[2018,9,14]]},"references-count":49,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2018,9,14]]}},"URL":"https:\/\/doi.org\/10.1371\/journal.pcbi.1006376","relation":{},"ISSN":["1553-7358"],"issn-type":[{"value":"1553-7358","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,9,14]]}}}