{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T04:21:21Z","timestamp":1787026881450,"version":"build-2736575974"},"reference-count":68,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2023,6,29]],"date-time":"2023-06-29T00:00:00Z","timestamp":1687996800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100006606","name":"Natural Science Foundation of Tianjin City","doi-asserted-by":"publisher","award":["22JCYBJC00470"],"award-info":[{"award-number":["22JCYBJC00470"]}],"id":[{"id":"10.13039\/501100006606","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Aging Neurosci."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Most patients with Alzheimer's disease (AD) have an insidious onset and frequently atypical clinical symptoms, which are considered a normal consequence of aging, making it difficult to diagnose AD medically. But then again, accurate diagnosis is critical to prevent degeneration and provide early treatment for AD patients.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>This study aims to establish a novel EEG-based classification framework with deep learning methods for AD recognition.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>First, considering the network interactions in different frequency bands (\u03b4, \u03b8, \u03b1, \u03b2, and \u03b3), multiplex networks are reconstructed by the phase synchronization index (PSI) method, and fourteen topology features are extracted subsequently, forming a high-dimensional feature vector. However, in feature combination, not all features can provide effective information for recognition. Moreover, combining features by manual selection is time-consuming and laborious. Thus, a feature selection optimization algorithm called MOPSO-GDM was proposed by combining multi-objective particle swarm optimization (MOPSO) algorithm with Gaussian differential mutation (GDM) algorithm. In addition to considering the classification error rates of support vector machine, naive bayes, and discriminant analysis classifiers, our algorithm also considers distance measure as an optimization objective.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Finally, this method proposed achieves an excellent classification error rate of 0.0531 (5.31%) with the feature vector size of 8, by a ten-fold cross-validation strategy.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>These findings show that our framework can adaptively combine the best brain network features to explore network synchronization, functional interactions, and characterize brain functional abnormalities, which can improve the recognition efficiency of diseases. While improving the classification accuracy of application algorithms, we aim to expand our understanding of the brain function of patients with neurological disorders through the analysis of brain networks.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fnagi.2023.1160534","type":"journal-article","created":{"date-parts":[[2023,6,29]],"date-time":"2023-06-29T10:30:31Z","timestamp":1688034631000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":9,"title":["A novel framework of MOPSO-GDM in recognition of Alzheimer's EEG-based functional network"],"prefix":"10.3389","volume":"15","author":[{"given":"Ruofan","family":"Wang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haodong","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lianshuan","family":"Shi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chunxiao","family":"Han","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qiguang","family":"He","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yanqiu","family":"Che","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Li","family":"Luo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2023,6,29]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"282","DOI":"10.1080\/23279095.2019.1630626","article-title":"Signs and symptoms method in neuropsychology: A preliminary investigation of a standardized clinical interview for assessment of cognitive decline in dementia","volume":"28","author":"Abbate","year":"2021","journal-title":"Appl. Neuropsychol"},{"key":"B2","doi-asserted-by":"publisher","first-page":"13","DOI":"10.18280\/ts.380102","article-title":"Depression detection based on geometrical features extracted from sodp shape of eeg signals and binary pso","volume":"38","author":"Akbari","year":"2021","journal-title":"Traitement Signal"},{"key":"B3","doi-asserted-by":"publisher","first-page":"7906","DOI":"10.1016\/j.egyr.2021.01.034","article-title":"A hybrid algorithm (bapso) for capacity configuration optimization in a distributed solar pv based microgrid","volume":"7","author":"Almadhor","year":"2021","journal-title":"Energy Reports"},{"key":"B4","doi-asserted-by":"publisher","DOI":"10.1109\/CEC.2015.7257190","article-title":"\u201cImage descriptor: A genetic programming approach to multiclass texture classification,\u201d","author":"Al-Sahaf","year":"2015","journal-title":"2015 IEEE Congress on Evolutionary Computation (CEC)"},{"key":"B5","doi-asserted-by":"publisher","first-page":"322","DOI":"10.1002\/ana.26411","article-title":"Increasing brain gamma activity improves episodic memory and restores cholinergic dysfunction in alzheimer's disease","volume":"92","author":"Benussi","year":"2022","journal-title":"Ann. Neurol"},{"key":"B6","doi-asserted-by":"publisher","first-page":"e78763","DOI":"10.1371\/journal.pone.0078763","article-title":"Functional brain networks: random, \u201csmall world\u201d or deterministic?","volume":"8","author":"Blinowska","year":"2013","journal-title":"PLoS ONE"},{"key":"B7","doi-asserted-by":"publisher","first-page":"1183","DOI":"10.1038\/s41593-020-0687-6","article-title":"Synergy between amyloid-\u03b2 and tau in alzheimer's disease","volume":"23","author":"Busche","year":"2020","journal-title":"Nat. Neurosci"},{"key":"B8","doi-asserted-by":"publisher","first-page":"490","DOI":"10.1016\/j.neucom.2018.07.019","article-title":"Reconstruction of functional brain network in alzheimer's disease via cross-frequency phase synchronization","volume":"314","author":"Cai","year":"2018","journal-title":"Neurocomputing"},{"key":"B9","doi-asserted-by":"publisher","DOI":"10.1063\/1.4929148","article-title":"Characterization of complexity in the electroencephalograph activity of alzheimer's disease based on fuzzy entropy","author":"Cao","year":"2015","journal-title":"Chaos"},{"key":"B10","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1016\/j.compeleceng.2013.11.024","article-title":"A survey on feature selection methods","volume":"40","author":"Chandrashekar","year":"2014","journal-title":"Comput. Electr. Eng"},{"key":"B11","doi-asserted-by":"publisher","first-page":"11766","DOI":"10.1016\/j.eswa.2012.04.075","article-title":"Association rule-based feature selection method for alzheimer's disease diagnosis","volume":"39","author":"Chaves","year":"2012","journal-title":"Expert Syst. Applic"},{"key":"B12","doi-asserted-by":"publisher","first-page":"446","DOI":"10.1109\/TEVC.2021.3100056","article-title":"Evolutionary multitasking for feature selection in high-dimensional classification via particle swarm optimization","volume":"26","author":"Chen","year":"2021","journal-title":"IEEE Trans. Evolut. Comput"},{"key":"B13","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1016\/j.compbiolchem.2007.09.005","article-title":"Improved binary pso for feature selection using gene expression data","volume":"32","author":"Chuang","year":"2008","journal-title":"Comput. Biol. Chem"},{"key":"B14","doi-asserted-by":"publisher","first-page":"1042","DOI":"10.1016\/j.neuroimage.2013.09.018","article-title":"A comparison of statistical methods for detecting context-modulated functional connectivity in fmri","volume":"84","author":"Cisler","year":"2014","journal-title":"Neuroimage"},{"key":"B15","doi-asserted-by":"publisher","first-page":"4545","DOI":"10.1007\/s11269-020-02656-8","article-title":"Hybrid algorithm of particle swarm optimization and grey wolf optimizer for reservoir operation management","volume":"34","author":"Dahmani","year":"2020","journal-title":"Water Resour. Manage"},{"key":"B16","doi-asserted-by":"publisher","first-page":"487","DOI":"10.2174\/156720510792231720","article-title":"Diagnosis of alzheimer's disease from eeg signals: where are we standing?","volume":"7","author":"Dauwels","year":"2010","journal-title":"Current Alzheimer Res"},{"key":"B17","doi-asserted-by":"publisher","DOI":"10.1063\/1.4917013","article-title":"Complexity extraction of electroencephalograms in alzheimer's disease with weighted-permutation entropy","author":"Deng","year":"2015","journal-title":"Chaos"},{"key":"B18","doi-asserted-by":"publisher","first-page":"288","DOI":"10.1016\/j.asoc.2017.06.004","article-title":"Study on an improved adaptive pso algorithm for solving multi-objective gate assignment","volume":"59","author":"Deng","year":"2017","journal-title":"Appl. Soft Comput"},{"key":"B19","doi-asserted-by":"publisher","first-page":"2164","DOI":"10.1109\/TNSRE.2020.3014951","article-title":"Topological network analysis of early alzheimer's disease based on resting-state eeg","volume":"28","author":"Duan","year":"2020","journal-title":"IEEE Trans. Neural Syst. Rehabilit. Eng"},{"key":"B20","doi-asserted-by":"publisher","first-page":"715","DOI":"10.3233\/JAD-190507","article-title":"Developing effective alzheimer's disease therapies: clinical experience and future directions","volume":"71","author":"Elmaleh","year":"2019","journal-title":"J. Alzheimer's Dis"},{"key":"B21","doi-asserted-by":"publisher","DOI":"10.1126\/science.abf3119","article-title":"Gamma rhythm communication between entorhinal cortex and dentate gyrus neuronal assemblies","author":"Fern\u00e1ndez-Ruiz","year":"2021","journal-title":"Science"},{"key":"B22","doi-asserted-by":"publisher","first-page":"369","DOI":"10.1007\/s11571-020-09626-1","article-title":"Complex networks and deep learning for eeg signal analysis","volume":"15","author":"Gao","year":"2021","journal-title":"Cogn. Neurodyn"},{"key":"B23","doi-asserted-by":"publisher","first-page":"1598","DOI":"10.1515\/jisys-2019-0062","article-title":"Binary genetic swarm optimization: A combination of ga and pso for feature selection","volume":"29","author":"Ghosh","year":"2020","journal-title":"J. Intell. Syst"},{"key":"B24","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1109\/TEVC.2016.2598858","article-title":"A multiobjective cooperative coevolutionary algorithm for hyperspectral sparse unmixing","volume":"21","author":"Gong","year":"2016","journal-title":"IEEE Trans. Evolut. Comput"},{"key":"B25","doi-asserted-by":"publisher","DOI":"10.3389\/fneur.2019.00756","article-title":"A novel ensemble-based machine learning algorithm to predict the conversion from mild cognitive impairment to alzheimer's disease using socio-demographic characteristics, clinical information, and neuropsychological measures","author":"Grassi","year":"2019","journal-title":"Front. Neurol"},{"key":"B26","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-13-1132-1_35","article-title":"\u201cA hybrid ga-pso algorithm to solve traveling salesman problem,\u201d","author":"Gupta","year":"2019","journal-title":"Computational Intelligence: Theories, Applications and Future Directions-Volume I: ICCI-2017"},{"key":"B27","doi-asserted-by":"publisher","DOI":"10.1109\/CEC.2018.8477771","article-title":"\u201cA multi-objective hybrid filter-wrapper evolutionary approach for feature construction on high-dimensional data,\u201d","author":"Hammami","year":"2018","journal-title":"2018 IEEE Congress on Evolutionary Computation (CEC)"},{"key":"B28","doi-asserted-by":"publisher","first-page":"66","DOI":"10.1038\/scientificamerican0792-66","article-title":"Genetic algorithms","volume":"267","author":"Holland","year":"1992","journal-title":"Sci. Am"},{"key":"B29","doi-asserted-by":"publisher","first-page":"155103","DOI":"10.1109\/ACCESS.2020.3018995","article-title":"A graph theory-based modeling of functional brain connectivity based on eeg: A systematic review in the context of neuroergonomics","volume":"8","author":"Ismail","year":"2020","journal-title":"IEEE Access"},{"key":"B30","doi-asserted-by":"publisher","first-page":"e35673","DOI":"10.1371\/journal.pone.0035673","article-title":"Eeg-based functional brain networks: does the network size matter?","volume":"7","author":"Joudaki","year":"2012","journal-title":"PLoS ONE"},{"key":"B31","doi-asserted-by":"publisher","DOI":"10.1109\/PACCS.2010.5627071","article-title":"\u201cA multiobjective aco algorithm for rough feature selection,\u201d","author":"Ke","year":"2010","journal-title":"2010 Second Pacific-Asia Conference on Circuits, Communications and System"},{"key":"B32","doi-asserted-by":"crossref","first-page":"1942","DOI":"10.1109\/ICNN.1995.488968","article-title":"\u201cParticle swarm optimization,\u201d","author":"Kennedy","year":"1995","journal-title":"Proceedings of ICNN'95-International Conference on Neural Networks"},{"key":"B33","doi-asserted-by":"publisher","first-page":"1060","DOI":"10.1016\/j.jksuci.2019.06.012","article-title":"Stability of feature selection algorithm: A review","volume":"34","author":"Khaire","year":"2022","journal-title":"J. King Saud Univ. Comput. Inf. Sci"},{"key":"B34","doi-asserted-by":"publisher","first-page":"1031","DOI":"10.3390\/electronics8091031","article-title":"High-density eeg signal processing based on active-source reconstruction for brain network analysis in alzheimer's disease","volume":"8","author":"La Foresta","year":"2019","journal-title":"Electronics"},{"key":"B35","doi-asserted-by":"publisher","first-page":"217","DOI":"10.1061\/(ASCE)WR.1943-5452.0000027","article-title":"Novel multiobjective shuffled frog leaping algorithm with application to reservoir flood control operation","volume":"136","author":"Li","year":"2010","journal-title":"J. Water Resour. Plann. Manage"},{"key":"B36","doi-asserted-by":"publisher","first-page":"164978","DOI":"10.1016\/j.ijleo.2020.164978","article-title":"Optimization of five-parameter brdf model based on hybrid ga-pso algorithm","volume":"219","author":"Liu","year":"2020","journal-title":"Optik"},{"key":"B37","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10916-019-1486-z","article-title":"Classification of depression patients and normal subjects based on electroencephalogram (eeg) signal using alpha power and theta asymmetry","volume":"44","author":"Mahato","year":"2020","journal-title":"J. Med. Syst"},{"key":"B38","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1016\/j.neuroscience.2021.01.002","article-title":"A transfer learning approach for early diagnosis of alzheimer's disease on mri images","volume":"460","author":"Mehmood","year":"2021","journal-title":"Neuroscience"},{"key":"B39","doi-asserted-by":"publisher","first-page":"3339","DOI":"10.17762\/turcomat.v12i2.2393","article-title":"Motor-imagery eeg signals classificationusing svm, mlp and lda classifiers","volume":"12","author":"Narayan","year":"2021","journal-title":"Turkish J. Comput. Mathem. EducTURCOMAT)"},{"key":"B40","doi-asserted-by":"publisher","DOI":"10.1055\/s-0042-1759580","article-title":"\u201cDementia prevention in clinical practice,\u201d","author":"Niotis","year":"2022","journal-title":"Seminars in Neurology"},{"key":"B41","doi-asserted-by":"publisher","first-page":"431","DOI":"10.1590\/1516-4446-2019-0735","article-title":"Early diagnosis and treatment of alzheimer's disease: new definitions and challenges","volume":"42","author":"Pais","year":"2020","journal-title":"Braz. J. Psychiat"},{"key":"B42","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TIM.2020.3019849","article-title":"Eeg-based multiclass workload identification using feature fusion and selection","volume":"70","author":"Pei","year":"2020","journal-title":"IEEE Trans. Instrument. Measur"},{"key":"B43","doi-asserted-by":"publisher","first-page":"e778","DOI":"10.1212\/WNL.0000000000007967","article-title":"Cognitive heterogeneity in probable alzheimer disease: Clinical and neuropathologic features","volume":"93","author":"Qiu","year":"2019","journal-title":"Neurology"},{"key":"B44","doi-asserted-by":"publisher","first-page":"415","DOI":"10.1016\/j.engappai.2017.06.025","article-title":"Selective cooperative disassembly planning based on multi-objective discrete artificial bee colony algorithm","volume":"64","author":"Ren","year":"2017","journal-title":"Eng. Applic. Artif. Intell"},{"key":"B45","doi-asserted-by":"publisher","first-page":"1059","DOI":"10.1016\/j.neuroimage.2009.10.003","article-title":"Complex network measures of brain connectivity: uses and interpretations","volume":"52","author":"Rubinov","year":"2010","journal-title":"Neuroimage"},{"key":"B46","doi-asserted-by":"publisher","first-page":"746","DOI":"10.1016\/j.dsp.2011.01.006","article-title":"Two approaches to selecting set of voxels for the diagnosis of alzheimer's disease using brain spect images","volume":"21","author":"Salas-Gonzalez","year":"2011","journal-title":"Digital Signal Proc"},{"key":"B47","doi-asserted-by":"publisher","DOI":"10.1109\/CEC.2007.4424687","article-title":"\u201cA closed loop stability analysis and parameter selection of the particle swarm optimization dynamics for faster convergence,\u201d","author":"Samal","year":"2007","journal-title":"2007 IEEE Congress on Evolutionary Computation"},{"key":"B48","doi-asserted-by":"publisher","first-page":"219","DOI":"10.1016\/j.cmpb.2019.06.029","article-title":"C-hmoshssa: Gene selection for cancer classification using multi-objective meta-heuristic and machine learning methods","volume":"178","author":"Sharma","year":"2019","journal-title":"Comput. Methods Programs Biomed"},{"key":"B49","doi-asserted-by":"publisher","DOI":"10.1109\/ISCID.2017.21","article-title":"\u201cHybrid metaheuristic for multi-objective steelmaking-continuous casting scheduling,\u201d","author":"Shuaipeng","year":"2017","journal-title":"2017 10th International Symposium on Computational Intelligence and Design (ISCID)"},{"key":"B50","doi-asserted-by":"publisher","first-page":"111","DOI":"10.31887\/DCNS.2018.20.2\/osporns","article-title":"Graph theory methods: applications in brain networks","volume":"20","author":"Sporns","year":"","journal-title":"Dial. Clin Neurosci"},{"key":"B51","doi-asserted-by":"publisher","first-page":"247","DOI":"10.31887\/DCNS.2013.15.3\/osporns","article-title":"Structure and function of complex brain networks","volume":"15","author":"Sporns","year":"","journal-title":"Dial. Clin Neurosci"},{"key":"B52","doi-asserted-by":"publisher","first-page":"1303","DOI":"10.3390\/ijerph16071303","article-title":"Application of artificial neural networks to identify alzheimer's disease using cerebral perfusion spect data","volume":"16","author":"Swietlik","year":"2019","journal-title":"Int. J. Environ. Res. Public Health"},{"key":"B53","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1016\/S0887-6177(03)00039-8","article-title":"Trail making test a and b: normative data stratified by age and education","volume":"19","author":"Tombaugh","year":"2004","journal-title":"Arch. Clin. Neuropsychol"},{"key":"B54","doi-asserted-by":"publisher","first-page":"1733","DOI":"10.1109\/TCYB.2017.2714145","article-title":"A new representation in pso for discretization-based feature selection","volume":"48","author":"Tran","year":"2017","journal-title":"IEEE Trans. Cybern"},{"key":"B55","doi-asserted-by":"publisher","first-page":"473","DOI":"10.1109\/TEVC.2018.2869405","article-title":"Variable-length particle swarm optimization for feature selection on high-dimensional classification","volume":"23","author":"Tran","year":"2018","journal-title":"IEEE Trans. Evolut. Comput"},{"key":"B56","doi-asserted-by":"publisher","first-page":"683","DOI":"10.1016\/j.tics.2013.09.012","article-title":"Network hubs in the human brain","volume":"17","author":"Van den Heuvel","year":"2013","journal-title":"Trends Cogn. Sci"},{"key":"B57","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1089\/brain.2020.0824","article-title":"Human brain networks in physiological and pathological aging: reproducibility of electroencephalogram graph theoretical analysis in cortical connectivity","volume":"12","author":"Vecchio","year":"2022","journal-title":"Brain Connect"},{"key":"B58","doi-asserted-by":"publisher","first-page":"1253","DOI":"10.3233\/JAD-200171","article-title":"Classification of alzheimer's disease with respect to physiological aging with innovative eeg biomarkers in a machine learning implementation","volume":"75","author":"Vecchio","year":"2020","journal-title":"J. Alzheimer's Dis"},{"key":"B59","doi-asserted-by":"publisher","first-page":"143","DOI":"10.1016\/j.neuroscience.2015.12.036","article-title":"Cortical connectivity and memory performance in cognitive decline: A study via graph theory from eeg data","volume":"316","author":"Vecchio","year":"2016","journal-title":"Neuroscience"},{"key":"B60","doi-asserted-by":"publisher","first-page":"1540","DOI":"10.3390\/e24111540","article-title":"Epileptic seizure detection using geometric features extracted from sodp shape of eeg signals and asylncpso-ga","volume":"24","author":"Wang","year":"2022","journal-title":"Entropy"},{"key":"B61","doi-asserted-by":"publisher","DOI":"10.1063\/1.4906038","article-title":"Multiple feature extraction and classification of electroencephalograph signal for alzheimers' with spectrum and bispectrum","author":"Wang","year":"","journal-title":"Chaos"},{"key":"B62","doi-asserted-by":"publisher","DOI":"10.1063\/1.4896095","article-title":"Decreased coherence and functional connectivity of electroencephalograph in alzheimer's disease","author":"Wang","year":"2014","journal-title":"Chaos"},{"key":"B63","doi-asserted-by":"publisher","first-page":"291","DOI":"10.1007\/s11571-014-9325-x","article-title":"Power spectral density and coherence analysis of alzheimer's eeg","volume":"9","author":"Wang","year":"","journal-title":"Cogn. Neurodyn"},{"key":"B64","doi-asserted-by":"publisher","DOI":"10.3390\/app122312110","article-title":"A hybrid multi-objective optimization method and its application to electromagnetic device designs","author":"Xie","year":"2022","journal-title":"Appl. Sci"},{"key":"B65","doi-asserted-by":"publisher","DOI":"10.3389\/fnins.2020.00641","article-title":"Identification of alzheimer's eeg with a wvg network-based fuzzy learning approach","author":"Yu","year":"2020","journal-title":"Front. Neurosci"},{"key":"B66","doi-asserted-by":"publisher","first-page":"1138","DOI":"10.1126\/science.aaa1934","article-title":"Cell types in the mouse cortex and hippocampus revealed by single-cell rna-seq","volume":"347","author":"Zeisel","year":"2015","journal-title":"Science"},{"key":"B67","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1016\/j.ins.2020.05.004","article-title":"Many-objective optimization of feature selection based on two-level particle cooperation","volume":"532","author":"Zhou","year":"2020","journal-title":"Inf. Sci"},{"key":"B68","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1016\/j.clinbiochem.2019.04.015","article-title":"Clinical aspects of alzheimer's disease","volume":"72","author":"Zv\u011b\u0159ov\u00e1","year":"2019","journal-title":"Clin. Biochem"}],"container-title":["Frontiers in Aging Neuroscience"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fnagi.2023.1160534\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,6,29]],"date-time":"2023-06-29T10:30:36Z","timestamp":1688034636000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fnagi.2023.1160534\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6,29]]},"references-count":68,"alternative-id":["10.3389\/fnagi.2023.1160534"],"URL":"https:\/\/doi.org\/10.3389\/fnagi.2023.1160534","relation":{},"ISSN":["1663-4365"],"issn-type":[{"value":"1663-4365","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,6,29]]},"article-number":"1160534"}}