{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,20]],"date-time":"2025-12-20T22:27:26Z","timestamp":1766269646422},"reference-count":26,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2019,4,10]],"date-time":"2019-04-10T00:00:00Z","timestamp":1554854400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Big Data"],"published-print":{"date-parts":[[2019,12]]},"DOI":"10.1186\/s40537-019-0190-7","type":"journal-article","created":{"date-parts":[[2019,4,10]],"date-time":"2019-04-10T12:13:29Z","timestamp":1554898409000},"update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":56,"title":["An intelligent Alzheimer\u2019s disease diagnosis method using unsupervised feature learning"],"prefix":"10.1186","volume":"6","author":[{"given":"Firouzeh","family":"Razavi","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mohammad Jafar","family":"Tarokh","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mahmood","family":"Alborzi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2019,4,10]]},"reference":[{"issue":"1","key":"190_CR1","doi-asserted-by":"publisher","first-page":"33","DOI":"10.1186\/s12920-015-0108-y","volume":"8","author":"A Alyass","year":"2015","unstructured":"Alyass A, Turcotte M, Meyre D. From big data analysis to personalized medicine for all: challenges and opportunities. BMC Med Genomics. 2015;8(1):33.","journal-title":"BMC Med Genomics"},{"issue":"2","key":"190_CR2","doi-asserted-by":"publisher","first-page":"171","DOI":"10.1007\/s11036-013-0489-0","volume":"19","author":"M Chen","year":"2014","unstructured":"Chen M, Mao S, Liu Y. Big data: a survey. Mob Netw Appl. 2014;19(2):171\u2013209.","journal-title":"Mob Netw Appl"},{"key":"190_CR3","first-page":"1","volume":"8","author":"J Luo","year":"2016","unstructured":"Luo J, Wu M, Gopukumar D, Zhao Y. Big data application in biomedical research and health care: a literature review. Biomed Inform Insights. 2016;8:1.","journal-title":"Biomed Inform Insights"},{"issue":"2","key":"190_CR4","doi-asserted-by":"publisher","first-page":"54","DOI":"10.1007\/s41019-016-0011-3","volume":"1","author":"S Siuly","year":"2016","unstructured":"Siuly S, Zhang Y. Medical big data: neurological diseases diagnosis through medical data analysis. Data Sci Eng. 2016;1(2):54\u201364.","journal-title":"Data Sci Eng"},{"issue":"11","key":"190_CR5","doi-asserted-by":"publisher","first-page":"1510","DOI":"10.1038\/nn.3818","volume":"17","author":"RA Poldrack","year":"2014","unstructured":"Poldrack RA, Gorgolewski KJ. Making big data open: data sharing in neuroimaging. Nat Neurosci. 2014;17(11):1510\u20137.","journal-title":"Nat Neurosci"},{"key":"190_CR6","doi-asserted-by":"crossref","unstructured":"Glenner GG. Alzheimer\u2019s disease. In: Biomedical advances in aging. Springer. 1990:51\u201362.","DOI":"10.1007\/978-1-4613-0513-2_5"},{"key":"190_CR7","unstructured":"Baum LW, Chow HLA, Cheng KK. Nanoparticle contrast agent for early diagnosis of alzheimer\u2019s disease by magnetic resonance imaging (mri). ed: Google Patents. 2016."},{"issue":"8","key":"190_CR8","doi-asserted-by":"publisher","first-page":"964","DOI":"10.1016\/j.jalz.2015.02.004","volume":"11","author":"O Sabri","year":"2015","unstructured":"Sabri O, et al. Florbetaben PET imaging to detect amyloid beta plaques in Alzheimer\u2019s disease: phase 3 study. Alzheimer\u2019s Dement. 2015;11(8):964\u201374.","journal-title":"Alzheimer\u2019s Dement"},{"key":"190_CR9","doi-asserted-by":"crossref","unstructured":"Li R et al. Deep learning based imaging data completion for improved brain disease diagnosis. In: International conference on medical image computing and computer-assisted intervention. Springer; 2014. pp. 305\u201312.","DOI":"10.1007\/978-3-319-10443-0_39"},{"key":"190_CR10","unstructured":"Socher R. Recursive deep learning for natural language processing and computer vision. Citeseer. 2014."},{"key":"190_CR11","volume-title":"Automatic speech recognition: a deep learning approach","author":"D Yu","year":"2014","unstructured":"Yu D, Deng L. Automatic speech recognition: a deep learning approach. Berlin: Springer; 2014."},{"key":"190_CR12","unstructured":"Bhatkoti P, Paul M. Early diagnosis of Alzheimer\u2019s disease: a multi-class deep learning framework with modified k-sparse autoencoder classification. In: Image and vision computing New Zealand (IVCNZ), 2016 international conference on, IEEE. 2016. pp. 1\u20135."},{"key":"190_CR13","doi-asserted-by":"crossref","unstructured":"Hu C, Ju R, Shen Y, Zhou P, Li Q. Clinical decision support for Alzheimer\u2019s disease based on deep learning and brain network. In: Communications (ICC), 2016 IEEE international conference on, IEEE. 2016. pp. 1\u20136.","DOI":"10.1109\/ICC.2016.7510831"},{"key":"190_CR14","unstructured":"Sarraf S, Tofighi G. Classification of Alzheimer\u2019s disease using fmri data and deep learning convolutional neural networks. arXiv preprint \n                    arXiv:1603.08631\n                    \n                  . 2016."},{"key":"190_CR15","doi-asserted-by":"publisher","first-page":"173","DOI":"10.1109\/JBHI.2017.2655720","volume":"22","author":"J Shi","year":"2017","unstructured":"Shi J, Zheng X, Li Y, Zhang Q, Ying S. Multimodal neuroimaging feature learning with multimodal stacked deep polynomial networks for diagnosis of Alzheimer\u2019s disease. IEEE J Biomed Health Inform. 2017;22:173\u201383.","journal-title":"IEEE J Biomed Health Inform"},{"issue":"5","key":"190_CR16","doi-asserted-by":"publisher","first-page":"2569","DOI":"10.1007\/s00429-015-1059-y","volume":"221","author":"HI Suk","year":"2016","unstructured":"Suk HI, Lee SW, Shen D, A. S. D. N. Initiative. Deep sparse multi-task learning for feature selection in Alzheimer\u2019s disease diagnosis. Brain Struct Funct. 2016;221(5):2569\u201387.","journal-title":"Brain Struct Funct"},{"key":"190_CR17","doi-asserted-by":"crossref","unstructured":"Tao S, Zhang T, Yang J, Wang X, Lu W. Bearing fault diagnosis method based on stacked autoencoder and softmax regression. In: Control conference (CCC), 2015 34th Chinese, IEEE. 2015. pp. 6331\u20135.","DOI":"10.1109\/ChiCC.2015.7260634"},{"key":"190_CR18","doi-asserted-by":"publisher","first-page":"101","DOI":"10.1016\/j.media.2017.01.008","volume":"37","author":"H-I Suk","year":"2017","unstructured":"Suk H-I, Lee S-W, Shen D, A. S. D. N. Initiative. Deep ensemble learning of sparse regression models for brain disease diagnosis. Med Image Anal. 2017;37:101\u201313.","journal-title":"Med Image Anal"},{"issue":"2","key":"190_CR19","doi-asserted-by":"publisher","first-page":"841","DOI":"10.1007\/s00429-013-0687-3","volume":"220","author":"H-I Suk","year":"2015","unstructured":"Suk H-I, Lee S-W, Shen D, A. S. D. N. Initiative. Latent feature representation with stacked auto-encoder for AD\/MCI diagnosis. Brain Struct Funct. 2015;220(2):841\u201359.","journal-title":"Brain Struct Funct"},{"key":"190_CR20","unstructured":"Sarraf S, Tofighi G. Classification of Alzheimer\u2019s disease structural MRI data by deep learning convolutional neural networks. arXiv preprint \n                    arXiv:1607.06583\n                    \n                  . 2016."},{"key":"190_CR21","unstructured":"Hosseini-Asl E, Gimel\u2019farb G, El-Baz A. Alzheimer\u2019s disease diagnostics by a deeply supervised adaptable 3D convolutional network. arXiv preprint \n                    arXiv:1607.00556\n                    \n                  . 2016."},{"key":"190_CR22","doi-asserted-by":"crossref","unstructured":"Brosch T, Tam R, A. s. D. N. Initiative. Manifold learning of brain MRIs by deep learning. In: International conference on medical image computing and computer-assisted intervention. Springer. 2013, pp. 633\u201340.","DOI":"10.1007\/978-3-642-40763-5_78"},{"key":"190_CR23","unstructured":"Ngiam J, Chen Z, Bhaskar SA, Koh PW, Ng AY. Sparse filtering. In: Advances in neural information processing systems. 2011. pp. 1125\u201333."},{"key":"190_CR24","doi-asserted-by":"crossref","unstructured":"Held E, Cape J, Tintle N. Comparing machine learning and logistic regression methods for predicting hypertension using a combination of gene expression and next-generation sequencing data. In: BMC proceedings. BioMed Central, vol. 10, no. 7. 2016. p. 34.","DOI":"10.1186\/s12919-016-0020-2"},{"key":"190_CR25","doi-asserted-by":"publisher","first-page":"1401","DOI":"10.1016\/j.neurobiolaging.2010.04.029","volume":"31","author":"S Risacher","year":"2010","unstructured":"Risacher S, et al. Alzheimer\u2019s disease neuroimaging initiative (ADNI). Neurobiol Aging. 2010;31:1401\u201318.","journal-title":"Neurobiol Aging"},{"issue":"1","key":"190_CR26","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1111\/j.1467-9868.2005.00532.x","volume":"68","author":"M Yuan","year":"2006","unstructured":"Yuan M, Lin Y. Model selection and estimation in regression with grouped variables. J R Stat Soc Ser B (Stat Methodol). 2006;68(1):49\u201367.","journal-title":"J R Stat Soc Ser B (Stat Methodol)"}],"container-title":["Journal of Big Data"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1186\/s40537-019-0190-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1186\/s40537-019-0190-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1186\/s40537-019-0190-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,4,8]],"date-time":"2020-04-08T23:05:58Z","timestamp":1586387158000},"score":1,"resource":{"primary":{"URL":"https:\/\/journalofbigdata.springeropen.com\/articles\/10.1186\/s40537-019-0190-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,4,10]]},"references-count":26,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2019,12]]}},"alternative-id":["190"],"URL":"https:\/\/doi.org\/10.1186\/s40537-019-0190-7","relation":{},"ISSN":["2196-1115"],"issn-type":[{"value":"2196-1115","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,4,10]]},"assertion":[{"value":"7 December 2018","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 March 2019","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 April 2019","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}],"article-number":"32"}}