{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T16:43:55Z","timestamp":1782837835821,"version":"3.54.5"},"reference-count":69,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2023,1,9]],"date-time":"2023-01-09T00:00:00Z","timestamp":1673222400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,1,9]],"date-time":"2023-01-09T00:00:00Z","timestamp":1673222400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"National Health and Medical Research Council of Australia","award":["350833"],"award-info":[{"award-number":["350833"]}]},{"name":"National Health and Medical Research Council of Australia","award":["350833"],"award-info":[{"award-number":["350833"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Bioinformatics"],"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Background<\/jats:title><jats:p>Feature selection is often used to identify the important features in a dataset but can produce unstable results when applied to high-dimensional data. The stability of feature selection can be improved with the use of feature selection ensembles, which aggregate the results of multiple base feature selectors. However, a threshold must be applied to the final aggregated feature set to separate the relevant features from the redundant ones. A fixed threshold, which is typically used, offers no guarantee that the final set of selected features contains only relevant features. This work examines a selection of data-driven thresholds to automatically identify the relevant features in an ensemble feature selector and evaluates their predictive accuracy and stability. Ensemble feature selection with data-driven thresholding is applied to two real-world studies of Alzheimer's disease. Alzheimer's disease is a progressive neurodegenerative disease with no known cure, that begins at least 2\u20133 decades before overt symptoms appear, presenting an opportunity for researchers to identify early biomarkers that might identify patients at risk of developing Alzheimer's disease.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The ensemble feature selectors, combined with data-driven thresholds, produced more stable results, on the whole, than the equivalent individual feature selectors, showing an improvement in stability of up to 34%. The most successful data-driven thresholds were the robust rank aggregation threshold and the threshold algorithm threshold from the field of information retrieval. The features identified by applying these methods to datasets from Alzheimer's disease studies reflect current findings in the AD literature.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>Data-driven thresholds applied to ensemble feature selectors provide more stable, and therefore more reproducible, selections of features than individual feature selectors, without loss of performance. The use of a data-driven threshold eliminates the need to choose a fixed threshold a-priori and can select a more meaningful set of features. A reliable and compact set of features can produce more interpretable models by identifying the factors that are important in understanding a disease.<\/jats:p><\/jats:sec>","DOI":"10.1186\/s12859-022-05132-9","type":"journal-article","created":{"date-parts":[[2023,1,9]],"date-time":"2023-01-09T15:05:22Z","timestamp":1673276722000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["Ensemble feature selection with data-driven thresholding for Alzheimer's disease biomarker discovery"],"prefix":"10.1186","volume":"24","author":[{"given":"Annette","family":"Spooner","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gelareh","family":"Mohammadi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Perminder S.","family":"Sachdev","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Henry","family":"Brodaty","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Arcot","family":"Sowmya","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"name":"for the Sydney Memory and Ageing Study and the Alzheimer\u2019s Disease Neuroimaging Initiative","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,1,9]]},"reference":[{"key":"5132_CR1","first-page":"1157","volume":"3","author":"I Guyon","year":"2003","unstructured":"Guyon I, Elisseeff A, De AM. An introduction to variable and feature selection. J Mach Learn Res. 2003;3:1157\u201382.","journal-title":"J Mach Learn Res"},{"key":"5132_CR2","doi-asserted-by":"crossref","unstructured":"Awada W, Khoshgoftaar TM, Dittman D, Wald R, Napolitano A. A review of the stability of feature selection techniques for bioinformatics data. In International Conference on Information Reuse & Integration (IRI) 2012;356\u201363.","DOI":"10.1109\/IRI.2012.6303031"},{"issue":"1","key":"5132_CR3","doi-asserted-by":"publisher","first-page":"95","DOI":"10.1007\/s10115-006-0040-8","volume":"12","author":"A Kalousis","year":"2007","unstructured":"Kalousis A, Prados J, Hilario M. Stability of feature selection algorithms: a study on high-dimensional spaces. Knowl Inf Syst. 2007;12(1):95\u2013116.","journal-title":"Knowl Inf Syst"},{"key":"5132_CR4","doi-asserted-by":"crossref","unstructured":"Yu L, Ding C, Loscalzo S. Stable feature selection via dense feature groups. Proceedings of the 14th ACM SIGKDD international conference on knowledge discovery and data mining. 2008;803\u201311.","DOI":"10.1145\/1401890.1401986"},{"issue":"19","key":"5132_CR5","doi-asserted-by":"publisher","first-page":"2507","DOI":"10.1093\/bioinformatics\/btm344","volume":"23","author":"Y Saeys","year":"2007","unstructured":"Saeys Y, Inza I, Larranaga P. A review of feature selection techniques in bioinformatics. Bioinformatics. 2007;23(19):2507\u201317.","journal-title":"Bioinformatics"},{"key":"5132_CR6","doi-asserted-by":"crossref","unstructured":"Saeys Y, Abeel T, Van de Peer Y. Robust feature selection using ensemble feature selection techniques. Mach Learn Knowl Discov Databases ECML PKDD 2008. 2008;","DOI":"10.1007\/978-3-540-87481-2_21"},{"key":"5132_CR7","doi-asserted-by":"publisher","unstructured":"Seijo-Pardo B, Bol\u00f3n-Canedo V, Alonso-Betanzos A. On developing an automatic threshold applied to feature selection ensembles. Inf Fusion. 2019;45(June 2017):227\u201345. doi:https:\/\/doi.org\/10.1016\/j.inffus.2018.02.007","DOI":"10.1016\/j.inffus.2018.02.007"},{"issue":"3","key":"5132_CR8","doi-asserted-by":"publisher","first-page":"242","DOI":"10.1093\/jamia\/ocy165","volume":"26","author":"X Song","year":"2019","unstructured":"Song X, Waitman LR, Hu Y, Yu ASL, Robins D, Liu M. Robust clinical marker identification for diabetic kidney disease with ensemble feature selection. J Am Med Inform Assoc. 2019;26(3):242\u201353.","journal-title":"J Am Med Inform Assoc"},{"key":"5132_CR9","doi-asserted-by":"publisher","unstructured":"Pes B. Ensemble feature selection for high-dimensional data: a stability analysis across multiple domains. Neural Comput Appl. 2019;3. Available from: https:\/\/doi.org\/10.1007\/s00521-019-04082-3","DOI":"10.1007\/s00521-019-04082-3"},{"issue":"13","key":"5132_CR10","doi-asserted-by":"publisher","first-page":"1766","DOI":"10.1093\/bioinformatics\/bts238","volume":"28","author":"VA Huynh-Thu","year":"2012","unstructured":"Huynh-Thu VA, Saeys Y, Wehenkel L, Geurts P. Statistical interpretation of machine learning-based feature importance scores for biomarker discovery. Bioinformatics. 2012;28(13):1766\u201374.","journal-title":"Bioinformatics"},{"key":"5132_CR11","doi-asserted-by":"crossref","unstructured":"Selkoe DJ, Hardy J. The amyloid hypothesis of Alzheimer\u2019s disease at 25 years. EMBO Mol Med. 2016;8(e201606210):1\u201314. Available from: http:\/\/www.ncbi.nlm.nih.gov\/pubmed\/27025652","DOI":"10.15252\/emmm.201606210"},{"key":"5132_CR12","doi-asserted-by":"crossref","unstructured":"Dietterich TG. Ensemble methods in machine learning. Lect Notes Comput Sci (including Subser Lect Notes Artif Intell Lect Notes Bioinformatics). 2000;1857 LNCS:1\u201315.","DOI":"10.1007\/3-540-45014-9_1"},{"issue":"3","key":"5132_CR13","doi-asserted-by":"publisher","first-page":"392","DOI":"10.1093\/bioinformatics\/btp630","volume":"26","author":"T Abeel","year":"2009","unstructured":"Abeel T, Helleputte T, Van de Peer Y, Dupont P, Saeys Y. Robust biomarker identification for cancer diagnosis with ensemble feature selection methods. Bioinformatics. 2009;26(3):392\u20138.","journal-title":"Bioinformatics"},{"key":"5132_CR14","doi-asserted-by":"crossref","unstructured":"Ben Brahim A, Limam M. Robust ensemble feature selection for high dimensional data sets. In Proceedings of 2013 International Conference on High Performance Computing & Simulation (HPCS), HPCS 2013. 2013;151\u20137.","DOI":"10.1109\/HPCSim.2013.6641406"},{"key":"5132_CR15","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1016\/j.neucom.2013.03.067","volume":"135","author":"V Bol\u00f3n-Canedo","year":"2014","unstructured":"Bol\u00f3n-Canedo V, S\u00e1nchez-Maro\u00f1o N, Alonso-Betanzos A. Data classification using an ensemble of filters. Neurocomputing. 2014;135:13\u201320.","journal-title":"Neurocomputing"},{"issue":"4","key":"5132_CR16","first-page":"1","volume":"12","author":"A Ben Brahim","year":"2017","unstructured":"Ben Brahim A, Limam M. Ensemble feature selection for high dimensional data: a new method and a comparative study. Adv Data Anal Classif. 2017;12(4):1\u201316.","journal-title":"Adv Data Anal Classif"},{"key":"5132_CR17","doi-asserted-by":"publisher","first-page":"124","DOI":"10.1016\/j.knosys.2016.11.017","volume":"118","author":"B Seijo-Pardo","year":"2017","unstructured":"Seijo-Pardo B, Porto-D\u00edaz I, Bol\u00f3n-Canedo V, Alonso-Betanzos A. Ensemble feature selection: Homogeneous and heterogeneous approaches. Knowl-Based Syst. 2017;118:124\u201339. https:\/\/doi.org\/10.1016\/j.knosys.2016.11.017.","journal-title":"Knowl-Based Syst"},{"key":"5132_CR18","doi-asserted-by":"crossref","unstructured":"Wald R, Khoshgoftaar TM, Dittman D, Awada W, Napolitano A. An extensive comparison of feature ranking aggregation techniques in bioinformatics. Proceedings of 2012 IEEE 13th International Conference on Information Reuse & Integration (IRI) 2012. 2012;377\u201384.","DOI":"10.1109\/IRI.2012.6303034"},{"key":"5132_CR19","first-page":"327","volume":"11906","author":"K Sechidis","year":"2019","unstructured":"Sechidis K, Papangelou K, Nogueira S, Weatherall J, Brown G. On the stability of feature selection in the presence of feature correlations. Mach Learn Knowl Discov Databases ECML PKDD. 2019;11906:327\u201342.","journal-title":"Mach Learn Knowl Discov Databases ECML PKDD"},{"issue":"12","key":"5132_CR20","doi-asserted-by":"publisher","first-page":"1983","DOI":"10.1080\/00949655.2010.511622","volume":"81","author":"M Zhu","year":"2011","unstructured":"Zhu M, Fan G. Variable selection by ensembles for the Cox model. J Stat Comput Simul. 2011;81(12):1983\u201392.","journal-title":"J Stat Comput Simul"},{"key":"5132_CR21","first-page":"1399","volume":"3","author":"H Stoppiglia","year":"2003","unstructured":"Stoppiglia H, Dreyfus G, Dubois R, Oussay Y. Ranking a random feature for variable and feature selection. J Mach Learn Res. 2003;3:1399\u2013414.","journal-title":"J Mach Learn Res"},{"key":"5132_CR22","first-page":"1341","volume":"10","author":"E Tuv","year":"2009","unstructured":"Tuv E, Borisov A, Runger G, Torkkola K. Feature selection with ensembles, artificial variables, and redundancy elimination. J Mach Learn Res. 2009;10:1341\u201366.","journal-title":"J Mach Learn Res"},{"issue":"4","key":"5132_CR23","doi-asserted-by":"publisher","first-page":"271","DOI":"10.3233\/FI-2010-288","volume":"101","author":"MB Kursa","year":"2010","unstructured":"Kursa MB, Jankowski A, Rudnicki WR. Boruta: a system for feature selection. Fundam Informaticae. 2010;101(4):271\u201385.","journal-title":"Fundam Informaticae"},{"key":"5132_CR24","doi-asserted-by":"crossref","unstructured":"Jin X, Han J. K-Means Clustering. Encycl Mach Learn Data Min. 2017;697\u2013700.","DOI":"10.1007\/978-1-4899-7687-1_432"},{"issue":"9","key":"5132_CR25","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1371\/journal.pone.0162259","volume":"11","author":"YP Raykov","year":"2016","unstructured":"Raykov YP, Boukouvalas A, Baig F, Little MA. What to do when K-means clustering fails: a simple yet principled alternative algorithm. PLoS ONE. 2016;11(9):1\u201328.","journal-title":"PLoS ONE"},{"issue":"1","key":"5132_CR26","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1002\/mcda.313","volume":"11","author":"EJ Emond","year":"2002","unstructured":"Emond EJ, Mason DW. A new rank correlation coefficient with application to the consensus ranking problem. J Multi-Criteria Decis Anal. 2002;11(1):17\u201328.","journal-title":"J Multi-Criteria Decis Anal"},{"key":"5132_CR27","unstructured":"Dunne K, Cunningham P, Azuaje F. Solutions to Instability Problems with Sequential Wrapper-based Approaches to Feature Selection. Mach Learn. 2002;1\u201322. Available from: http:\/\/citeseerx.ist.psu.edu\/viewdoc\/download?doi=10.1.1.11.4109&rep=rep1&type=pdf"},{"key":"5132_CR28","doi-asserted-by":"crossref","unstructured":"Aslam JA, Montague M. Models for metasearch. SIGIR Forum (ACM Spec Interes Gr Inf Retrieval). 2001;276\u201384.","DOI":"10.1145\/383952.384007"},{"issue":"4","key":"5132_CR29","doi-asserted-by":"publisher","first-page":"573","DOI":"10.1093\/bioinformatics\/btr709","volume":"28","author":"R Kolde","year":"2012","unstructured":"Kolde R, Laur S, Adler P, Vilo J. Robust rank aggregation for gene list integration and meta-analysis. Bioinformatics. 2012;28(4):573\u201380.","journal-title":"Bioinformatics"},{"issue":"4","key":"5132_CR30","doi-asserted-by":"publisher","first-page":"614","DOI":"10.1016\/S0022-0000(03)00026-6","volume":"66","author":"R Fagin","year":"2003","unstructured":"Fagin R, Lotem A, Naor M. Optimal aggregation algorithms for middleware. J Comput Syst Sci. 2003;66(4):614\u201356.","journal-title":"J Comput Syst Sci"},{"key":"5132_CR31","doi-asserted-by":"crossref","unstructured":"Sculley D. Rank Aggregation for Similar Items. 2006; Available from: http:\/\/www.eecs.tufts.edu\/~dsculley\/papers\/mergeSimilarRank.pdf","DOI":"10.1137\/1.9781611972771.66"},{"issue":"11","key":"5132_CR32","doi-asserted-by":"publisher","first-page":"1921","DOI":"10.1109\/TPAMI.2010.34","volume":"32","author":"P Somol","year":"2010","unstructured":"Somol P, Novovi\u010dov\u00e1 J. Evaluating stability and comparing output of feature selectors that optimize feature subset cardinality. IEEE Trans Pattern Anal Mach Intell. 2010;32(11):1921\u201339.","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"5132_CR33","unstructured":"Lustgarten JL, Gopalakrishnan V, Visweswaran S. Measuring stability of feature selection in biomedical datasets. AMIA . Annu Symp proceedings AMIA Symp. 2009;2009(3):406\u201310."},{"key":"5132_CR34","doi-asserted-by":"crossref","unstructured":"Sachdev PS, Brodaty H, Reppermund S, Kochan N a, Trollor JN, Draper B, et al. The Sydney Memory and Ageing Study (MAS): methodology and baseline medical and neuropsychiatric characteristics of an elderly epidemiological non-demented cohort of Australians aged 70\u201390 years. Int Psychogeriatr. 2010;22(8):1248\u201364.","DOI":"10.1017\/S1041610210001067"},{"issue":"4","key":"5132_CR35","doi-asserted-by":"publisher","first-page":"869","DOI":"10.1016\/j.nic.2005.09.008","volume":"15","author":"W Mueller","year":"2005","unstructured":"Mueller W, Thal P. The Alzheimer\u2019s disease neuroimaging initiative. Neuroimaging Clin N Am. 2005;15(4):869\u2013xii.","journal-title":"Neuroimaging Clin N Am"},{"key":"5132_CR36","doi-asserted-by":"crossref","unstructured":"van Buuren S, Groothuis-Oudshoorn K. mice. Multivariate Imputation by Chained Equations in R. J Stat Softw. 2011;45(3). Available from: http:\/\/www.jstatsoft.org\/v45\/i03\/","DOI":"10.18637\/jss.v045.i03"},{"key":"5132_CR37","doi-asserted-by":"publisher","unstructured":"Spooner A, Sowmya A, Sachdev P, Kochan NA, Trollor J, Brodaty H. Machine learning models for predicting dementia: a comparison of methods for survival analysis of high-dimensional clinical data. Nat Sci Rep. 2020;1\u201310. doi:https:\/\/doi.org\/10.1038\/s41598-020-77220-w.","DOI":"10.1038\/s41598-020-77220-w"},{"key":"5132_CR38","unstructured":"Team R. R: A language and environment for statistical computing (Version 3.4. 2) [Computer software]. Vienna, Austria: R Foundation for Statistical Computing. 2017."},{"key":"5132_CR39","unstructured":"Bischl B, Lang M, Kotthoff L, Schiffner J, Richter J, Studerus E, et al. mlr: machine learning in R. J Mach Learn Res. 2016;17(1):5938\u201342. Available from: https:\/\/dl.acm.org\/citation.cfm?id=3053452"},{"key":"5132_CR40","unstructured":"Katana Computational Cluster. https:\/\/dx.oi.org\/1026190\/669x-a286."},{"issue":"16","key":"5132_CR41","first-page":"385","volume":"1997","author":"RJ Tibshirani","year":"1995","unstructured":"Tibshirani RJ. The lasso method for variable selection in the Cox model. Stat Med. 1995;1997(16):385\u201395.","journal-title":"Stat Med"},{"key":"5132_CR42","doi-asserted-by":"crossref","unstructured":"Simon N, Friedman J, Hastie T, Tibrishani R. Regularization paths for Cox\u2019s proportional hazards model via coordinate descent. 2011;(1):1\u201313.","DOI":"10.18637\/jss.v033.i01"},{"issue":"12","key":"5132_CR43","doi-asserted-by":"publisher","first-page":"6044","DOI":"10.1016\/j.csda.2006.11.041","volume":"51","author":"G Tutz","year":"2007","unstructured":"Tutz G, Binder H. Boosting ridge regression. Comput Stat Data Anal. 2007;51(12):6044\u201359.","journal-title":"Comput Stat Data Anal"},{"key":"5132_CR44","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/1471-2105-9-14","volume":"9","author":"H Binder","year":"2008","unstructured":"Binder H, Schumacher M. Allowing for mandatory covariates in boosting estimation of sparse high-dimensional survival models. BMC Bioinform. 2008;9:1\u201310.","journal-title":"BMC Bioinform"},{"issue":"8","key":"5132_CR45","doi-asserted-by":"publisher","first-page":"1272","DOI":"10.1002\/sim.7212","volume":"36","author":"MN Wright","year":"2017","unstructured":"Wright MN, Dankowski T, Ziegler A. Unbiased split variable selection for random survival forests using maximally selected rank statistics. Stat Med. 2017;36(8):1272\u201384.","journal-title":"Stat Med"},{"key":"5132_CR46","doi-asserted-by":"publisher","unstructured":"Silverman BW. Density estimation for statistics and data analysis. Routledge; 1998. 176 p. Available from: https:\/\/doi.org\/10.1201\/9781315140919","DOI":"10.1201\/9781315140919"},{"issue":"18","key":"5132_CR47","doi-asserted-by":"publisher","first-page":"2543","DOI":"10.1001\/jama.1982.03320430047030","volume":"247","author":"FE Harrell","year":"1982","unstructured":"Harrell FE, Califf RM, Pryor DB, Lee KL, Rosati RA. Evaluating the yield of medical tests. JAMA J Am Med Assoc. 1982;247(18):2543\u20136.","journal-title":"JAMA J Am Med Assoc"},{"issue":"3","key":"5132_CR48","doi-asserted-by":"publisher","first-page":"227","DOI":"10.1212\/WNL.0b013e318225c6bc","volume":"77","author":"A Mitnitski","year":"2011","unstructured":"Mitnitski A, Rockwood K, Song X. Nontraditional risk factors combine to predict Alzheimer disease and dementia. Neurology. 2011;77(3):227\u201334.","journal-title":"Neurology"},{"issue":"10248","key":"5132_CR49","doi-asserted-by":"publisher","first-page":"413","DOI":"10.1016\/S0140-6736(20)30367-6","volume":"396","author":"G Livingston","year":"2020","unstructured":"Livingston G, Huntley J, Sommerlad A, Ames D, Ballard C, Banerjee S, et al. Dementia prevention, intervention, and care: 2020 report of the Lancet Commission. Lancet. 2020;396(10248):413\u201346.","journal-title":"Lancet"},{"issue":"3","key":"5132_CR50","doi-asserted-by":"publisher","first-page":"530","DOI":"10.1046\/j.1532-5415.2002.50122.x","volume":"50","author":"H Brodaty","year":"2002","unstructured":"Brodaty H, Pond D, Kemp NM, Luscombe G, Harding L, Berman K, et al. The GPCOG: a new screening test for dementia designed for general practice. J Am Geriatr Soc. 2002;50(3):530\u20134.","journal-title":"J Am Geriatr Soc"},{"key":"5132_CR51","doi-asserted-by":"publisher","first-page":"189","DOI":"10.1016\/0022-3956(75)90026-6","volume":"12","author":"M Folstein","year":"1975","unstructured":"Folstein M, Folstein S, McHugh P. Mini-mental state: a practical method for grading the cognitive stats of patients for the clinician. J Psychiatr Res. 1975;12:189\u201398.","journal-title":"J Psychiatr Res"},{"key":"5132_CR52","doi-asserted-by":"crossref","unstructured":"Cherbuin N, Francis Jorm A. The informant Questionnaire on cognitive decline in the elderly (IQCODE). Princ Pract Geriatr Psychiatry Third Ed. 2010;147\u201351.","DOI":"10.1002\/9780470669600.ch28"},{"key":"5132_CR53","doi-asserted-by":"publisher","first-page":"S546","DOI":"10.1016\/j.jalz.2011.05.1540","volume":"7","author":"M Slavin","year":"2011","unstructured":"Slavin M, Brodaty H, Kochan N, Crawford J, Reppermund S, Trollor J, et al. P3\u2013100: predicting MCI or dementia at follow-up: using subjective memory and non-memory complaints from both the participant and informant. Alzheimer\u2019s Dement. 2011;7:S546\u2013S546.","journal-title":"Alzheimer\u2019s Dement"},{"issue":"1","key":"5132_CR54","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13195-020-00736-w","volume":"13","author":"S Bayat","year":"2021","unstructured":"Bayat S, Babulal GM, Schindler SE, Fagan AM, Morris JC, Mihailidis A, et al. GPS driving: a digital biomarker for preclinical Alzheimer disease. Alzheimer\u2019s Res Ther. 2021;13(1):1\u20139.","journal-title":"Alzheimer\u2019s Res Ther"},{"key":"5132_CR55","doi-asserted-by":"crossref","unstructured":"Di X, Shi R, Diguiseppi C, Eby DW, Hill LL, Mielenz TJ, et al. Using naturalistic driving data to predict mild cognitive impairment and dementia: preliminary findings from the longitudinal research on aging drivers (longroad) study. Geriatr. 2021;6(2):0\u20139.","DOI":"10.3390\/geriatrics6020045"},{"issue":"1","key":"5132_CR56","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12916-015-0400-x","volume":"13","author":"A Mitnitski","year":"2015","unstructured":"Mitnitski A, Collerton J, Martin-Ruiz C, Jagger C, von Zglinicki T, Rockwood K, et al. Age-related frailty and its association with biological markers of ageing. BMC Med. 2015;13(1):1\u20139.","journal-title":"BMC Med"},{"issue":"4","key":"5132_CR57","first-page":"1","volume":"12","author":"PS Sangha","year":"2020","unstructured":"Sangha PS, Thakur M, Akhtar Z, Ramani S, Gyamfi RS. The Link between rheumatoid arthritis and dementia: a review. Cureus. 2020;12(4):1\u20138.","journal-title":"Cureus"},{"issue":"12","key":"5132_CR58","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1371\/journal.pone.0168106","volume":"11","author":"LT Kao","year":"2016","unstructured":"Kao LT, Kang JH, Lin HC, Huang CC, Lee HC, Chung SD. Rheumatoid arthritis was negatively associated with Alzheimer\u2019s disease: a population-based case-control study. PLoS ONE. 2016;11(12):1\u20139.","journal-title":"PLoS ONE"},{"issue":"June","key":"5132_CR59","first-page":"1","volume":"12","author":"JW Kim","year":"2020","unstructured":"Kim JW, Byun MS, Yi D, Lee JH, Jeon SY, Ko K, et al. Serum uric acid, Alzheimer-related brain changes, and cognitive impairment. Front Aging Neurosci. 2020;12(June):1\u20139.","journal-title":"Front Aging Neurosci"},{"key":"5132_CR60","unstructured":"Guo H, Sapra A. Instrumental Activity of Daily Living. [Internet]. StatPearls. Treasure Island (FL): StatPearls Publishing; 2021 [cited 2022 Jun 28]. Available from: https:\/\/www.ncbi.nlm.nih.gov\/books\/NBK553126\/"},{"key":"5132_CR61","doi-asserted-by":"crossref","unstructured":"Blennow K, Mattsson N, Sch\u00f6ll M, Hansson O, Zetterberg H. Amyloid biomarkers in Alzheimer\u2019s disease [Internet]. Vol. 36, Trends in pharmacological sciences. 2015 [cited 2017 May 5]. p. 297\u2013309. Available from: http:\/\/www.sciencedirect.com\/science\/article\/pii\/S0165614715000425","DOI":"10.1016\/j.tips.2015.03.002"},{"issue":"1","key":"5132_CR62","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13195-017-0329-8","volume":"10","author":"P Lewczuk","year":"2018","unstructured":"Lewczuk P, Ermann N, Andreasson U, Schultheis C, Podhorna J, Spitzer P, et al. Plasma neurofilament light as a potential biomarker of neurodegeneration in Alzheimer\u2019s disease. Alzheimer\u2019s Res Ther. 2018;10(1):1\u201310.","journal-title":"Alzheimer\u2019s Res Ther"},{"issue":"1","key":"5132_CR63","first-page":"1","volume":"12","author":"K Dhiman","year":"2020","unstructured":"Dhiman K, Gupta VB, Villemagne VL, Eratne D, Graham PL, Fowler C, et al. Cerebrospinal fluid neurofilament light concentration predicts brain atrophy and cognition in Alzheimer\u2019s disease. Alzheimer\u2019s Dement Diagnosis Assess Dis Monit. 2020;12(1):1\u20139.","journal-title":"Alzheimer\u2019s Dement Diagnosis Assess Dis Monit"},{"key":"5132_CR64","doi-asserted-by":"publisher","unstructured":"Zhou J, Liu J, Narayan VA, Ye J. Modeling disease progression via multi-task learning. Neuroimage. 2013;78:233\u201348. Available from: http:\/\/dx.doi.org\/https:\/\/doi.org\/10.1016\/j.neuroimage.2013.03.073","DOI":"10.1016\/j.neuroimage.2013.03.073"},{"issue":"2","key":"5132_CR65","doi-asserted-by":"publisher","first-page":"73","DOI":"10.1177\/0891988719882102","volume":"33","author":"J Cummings","year":"2020","unstructured":"Cummings J. The neuropsychiatric inventory: development and applications. J Geriatr Psychiatry Neurol. 2020;33(2):73\u201384.","journal-title":"J Geriatr Psychiatry Neurol"},{"key":"5132_CR66","doi-asserted-by":"crossref","unstructured":"Wellington H, Paterson RW, Portelius E, T\u00f6rnqvist U, Magdalinou N, Fox NC, et al. Increased CSF neurogranin concentration is specific to Alzheimer disease. 2016;","DOI":"10.1212\/WNL.0000000000002423"},{"key":"5132_CR67","doi-asserted-by":"publisher","unstructured":"Dafsari FS, Jessen F. Depression: an underrecognized target for prevention of dementia in Alzheimer\u2019s disease. Transl Psychiatry. 2020;10(1):1\u201313. Available from: http:\/\/dx.doi.org\/https:\/\/doi.org\/10.1038\/s41398-020-0839-1","DOI":"10.1038\/s41398-020-0839-1"},{"issue":"2","key":"5132_CR68","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1159\/000341583","volume":"34","author":"ME Kuyumcu","year":"2012","unstructured":"Kuyumcu ME, Yesil Y, Ozt\u00fcrk ZA, Kizilarslano\u01e7lu C, Etg\u00fcl S, Halil M, et al. The evaluation of neutrophil-lymphocyte ratio in Alzheimer\u2019s disease. Dement Geriatr Cogn Disord. 2012;34(2):69\u201374.","journal-title":"Dement Geriatr Cogn Disord"},{"issue":"5","key":"5132_CR69","doi-asserted-by":"publisher","first-page":"2055","DOI":"10.1214\/15-AOS1337","volume":"43","author":"RF Barber","year":"2015","unstructured":"Barber RF, Cand\u00e9s EJ. Controlling the false discovery rate via knockoffs. Ann Stat. 2015;43(5):2055\u201385.","journal-title":"Ann Stat"}],"container-title":["BMC Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12859-022-05132-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12859-022-05132-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12859-022-05132-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,3,20]],"date-time":"2023-03-20T08:10:53Z","timestamp":1679299853000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcbioinformatics.biomedcentral.com\/articles\/10.1186\/s12859-022-05132-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1,9]]},"references-count":69,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2023,12]]}},"alternative-id":["5132"],"URL":"https:\/\/doi.org\/10.1186\/s12859-022-05132-9","relation":{},"ISSN":["1471-2105"],"issn-type":[{"value":"1471-2105","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,1,9]]},"assertion":[{"value":"28 September 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 December 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 January 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The Human Research Ethics Committees of the University of New South Wales and the South Eastern Sydney and Illawarra Area Health Service granted ethics approval for the MAS study and written consent was given by all participants and informants. The MAS study and this work were carried out in accordance with the MAS Governance guidelines, which are based on relevant University of New South Wales and National Health and Medical Research Council research and ethics policies. The ADNI study was approved by the Institutional Review Boards of all participating institutions, and informed written consent was obtained from all participants at each site. Full details can be found at.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare that they have no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"9"}}