{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,12]],"date-time":"2026-09-12T01:20:50Z","timestamp":1789176050148,"version":"build-2803163510"},"reference-count":81,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2022,7,18]],"date-time":"2022-07-18T00:00:00Z","timestamp":1658102400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"megagrant of the Ministry of Science and Higher Education of the Russian Federation","award":["075-10-2022-090"],"award-info":[{"award-number":["075-10-2022-090"]}]},{"name":"megagrant of the Ministry of Science and Higher Education of the Russian Federation","award":["075-10-2019-083"],"award-info":[{"award-number":["075-10-2019-083"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJMS"],"abstract":"<jats:p>Early recognition of the risk of Alzheimer\u2019s disease (AD) onset is a global challenge that requires the development of reliable and affordable screening methods for wide-scale application. Proteomic studies of blood plasma are of particular relevance; however, the currently proposed differentiating markers are poorly consistent. The targeted quantitative multiple reaction monitoring (MRM) assay of the reported candidate biomarkers (CBs) can contribute to the creation of a consistent marker panel. An MRM-MS analysis of 149 nondepleted EDTA\u2013plasma samples (MHRC, Russia) of patients with AD (n = 47), mild cognitive impairment (MCI, n = 36), vascular dementia (n = 8), frontotemporal dementia (n = 15), and an elderly control group (n = 43) was performed using the BAK 125 kit (MRM Proteomics Inc., Canada). Statistical analysis revealed a significant decrease in the levels of afamin, apolipoprotein E, biotinidase, and serum paraoxonase\/arylesterase 1 associated with AD. Different training algorithms for machine learning were performed to identify the protein panels and build corresponding classifiers for the AD prognosis. Machine learning revealed 31 proteins that are important for AD differentiation and mostly include reported earlier CBs. The best-performing classifiers reached 80% accuracy, 79.4% sensitivity and 83.6% specificity and were able to assess the risk of developing AD over the next 3 years for patients with MCI. Overall, this study demonstrates the high potential of the MRM approach combined with machine learning to confirm the significance of previously identified CBs and to propose consistent protein marker panels.<\/jats:p>","DOI":"10.3390\/ijms23147907","type":"journal-article","created":{"date-parts":[[2022,7,18]],"date-time":"2022-07-18T11:32:23Z","timestamp":1658143943000},"page":"7907","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":32,"title":["Prognosis of Alzheimer\u2019s Disease Using Quantitative Mass Spectrometry of Human Blood Plasma Proteins and Machine Learning"],"prefix":"10.3390","volume":"23","author":[{"given":"Alexey S.","family":"Kononikhin","sequence":"first","affiliation":[{"name":"Center for Molecular and Cellular Biology, Skolkovo Institute of Science and Technology, 121205 Moscow, Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Natalia V.","family":"Zakharova","sequence":"additional","affiliation":[{"name":"Emanuel Institute for Biochemical Physics, Russian Academy of Sciences, 119334 Moscow, Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Savva D.","family":"Semenov","sequence":"additional","affiliation":[{"name":"Moscow Institute of Physics and Technology, 141700 Dolgoprudny, Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anna E.","family":"Bugrova","sequence":"additional","affiliation":[{"name":"Center for Molecular and Cellular Biology, Skolkovo Institute of Science and Technology, 121205 Moscow, Russia"},{"name":"Emanuel Institute for Biochemical Physics, Russian Academy of Sciences, 119334 Moscow, Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alexander G.","family":"Brzhozovskiy","sequence":"additional","affiliation":[{"name":"Center for Molecular and Cellular Biology, Skolkovo Institute of Science and Technology, 121205 Moscow, Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8177-7449","authenticated-orcid":false,"given":"Maria I.","family":"Indeykina","sequence":"additional","affiliation":[{"name":"Center for Molecular and Cellular Biology, Skolkovo Institute of Science and Technology, 121205 Moscow, Russia"},{"name":"Emanuel Institute for Biochemical Physics, Russian Academy of Sciences, 119334 Moscow, Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yana B.","family":"Fedorova","sequence":"additional","affiliation":[{"name":"Mental Health Research Center, 115522 Moscow, Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Igor V.","family":"Kolykhalov","sequence":"additional","affiliation":[{"name":"Mental Health Research Center, 115522 Moscow, Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6513-8009","authenticated-orcid":false,"given":"Polina A.","family":"Strelnikova","sequence":"additional","affiliation":[{"name":"Emanuel Institute for Biochemical Physics, Russian Academy of Sciences, 119334 Moscow, Russia"},{"name":"Moscow Institute of Physics and Technology, 141700 Dolgoprudny, Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8434-5916","authenticated-orcid":false,"given":"Anna Yu.","family":"Ikonnikova","sequence":"additional","affiliation":[{"name":"Center for Precision Genome Editing and Genetic Technologies for Biomedicine, Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, 119991 Moscow, Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3183-318X","authenticated-orcid":false,"given":"Dmitry A.","family":"Gryadunov","sequence":"additional","affiliation":[{"name":"Center for Precision Genome Editing and Genetic Technologies for Biomedicine, Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, 119991 Moscow, Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Svetlana I.","family":"Gavrilova","sequence":"additional","affiliation":[{"name":"Mental Health Research Center, 115522 Moscow, Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Evgeny N.","family":"Nikolaev","sequence":"additional","affiliation":[{"name":"Center for Molecular and Cellular Biology, Skolkovo Institute of Science and Technology, 121205 Moscow, Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,7,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1016\/j.jalz.2012.11.007","article-title":"The global prevalence of dementia: A systematic review and metaanalysis","volume":"9","author":"Prince","year":"2013","journal-title":"Alzheimer\u2019s Dement."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Alzheimer\u2019s Association (2016). 2016 Alzheimer\u2019s disease facts and figures. Alzheimer\u2019s Dement., 12, 459\u2013509.","DOI":"10.1016\/j.jalz.2016.03.001"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"916","DOI":"10.1038\/nature08538","article-title":"Multimodal techniques for diagnosis and prognosis of Alzheimer\u2019s disease","volume":"461","author":"Perrin","year":"2009","journal-title":"Nature"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"192","DOI":"10.1515\/CCLM.2006.035","article-title":"Total tau protein, phosphorylated tau (181p) protein, \u03b2-amyloid1-42, and \u03b2-amyloid1-40 in cerebrospinal fluid of patients with dementia with Lewy bodies","volume":"44","author":"Mollenhauer","year":"2006","journal-title":"Clin. Chem. Lab. Med."},{"key":"ref_5","first-page":"CD10803","article-title":"CSF tau and the CSF tau\/ABeta ratio for the diagnosis of Alzheimer\u2019s disease dementia and other dementias in people with mild cognitive impairment (MCI)","volume":"3","author":"Ritchie","year":"2013","journal-title":"Cochrane Database Syst. Rev."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"843","DOI":"10.1007\/s00702-012-0772-4","article-title":"Longitudinal plasma amyloid beta as a biomarker of Alzheimer\u2019s disease","volume":"119","author":"Rissman","year":"2012","journal-title":"J. Neural. Transm."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1284","DOI":"10.1016\/j.neurobiolaging.2010.05.003","article-title":"3D PIB and CSF biomarker associations with hippocampal atrophy in ADNI subjects","volume":"31","author":"Apostolova","year":"2010","journal-title":"Neurobiol. Aging"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1007\/s12149-009-0330-9","article-title":"Amyloid-plaque imaging in early and differential diagnosis of dementia","volume":"24","author":"Drzezga","year":"2010","journal-title":"Ann. Nucl. Med."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1007\/s00259-012-2237-2","article-title":"A European multicentre PET study of fibrillar amyloid in Alzheimer\u2019s disease","volume":"40","author":"Norberg","year":"2013","journal-title":"Eur. J. Nucl. Med. Mol. Imaging"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1212\/WNL.0b013e31823efc6c","article-title":"MRI cortical thickness biomarker predicts AD-like CSF and cognitive decline in normal adults","volume":"78","author":"Dickerson","year":"2012","journal-title":"Neurology"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1016\/j.jalz.2013.01.013","article-title":"The future of blood-based biomarkers for Alzheimer\u2019s disease","volume":"10","author":"Henriksen","year":"2014","journal-title":"Alzheimer\u2019s Dement."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"927","DOI":"10.1016\/j.biopsych.2009.01.027","article-title":"Beta amyloid in Alzheimer\u2019s disease: Increased deposition in brain is reflected in reduced concentration in cerebrospinal fluid","volume":"65","author":"Grimmer","year":"2009","journal-title":"Biol. Psychiatry"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"343","DOI":"10.1586\/14789450.2015.1065183","article-title":"Amyloid-\u03b2 as a biomarker for Alzheimer\u2019s disease: Quantification methods in body fluids","volume":"12","author":"Galozzi","year":"2015","journal-title":"Expert Rev. Proteom."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1001\/jamaneurol.2015.3135","article-title":"Combined plasma and cerebrospinal fluid signature for the prediction of midterm progression from mild cognitive impairment to Alzheimer disease","volume":"73","author":"Lehallier","year":"2016","journal-title":"JAMA Neurol."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"26801","DOI":"10.1038\/srep26801","article-title":"Plasma beta-amyloid in Alzheimer\u2019s disease and vascular disease","volume":"6","author":"Janelidze","year":"2016","journal-title":"Sci. Rep."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"430","DOI":"10.1002\/1531-8249(200004)47:4<430::AID-ANA5>3.0.CO;2-I","article-title":"Use of structural magnetic resonance imaging to predict who will get Alzheimer\u2019s disease","volume":"47","author":"Killiany","year":"2000","journal-title":"Ann. Neurol."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1397","DOI":"10.1212\/WNL.52.7.1397","article-title":"Prediction of AD with MRI-based hippocampal volume in mild cognitive impairment","volume":"52","author":"Jack","year":"1999","journal-title":"Neurology"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1111\/j.1742-1241.2011.02845.x","article-title":"Diagnostic accuracy of 18 F-FDG and 11 C-PIB\u2013PET for prediction of short-term conversion to Alzheimer\u2019s disease in subjects with mild cognitive impairment","volume":"66","author":"Zhang","year":"2012","journal-title":"Int. J. Clin. Pract."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"403","DOI":"10.1002\/ana.21610","article-title":"Alzheimer\u2019s Disease Neuroimaging Initiative. Cerebrospinal fluid biomarker signature in Alzheimer\u2019s Disease Neuroimaging Initiative subjects","volume":"65","author":"Shaw","year":"2009","journal-title":"Ann. Neurol."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"639","DOI":"10.1038\/s41582-018-0079-7","article-title":"Blood-based biomarkers for Alzheimer disease: Mapping the road to the clinic","volume":"14","author":"Hampel","year":"2018","journal-title":"Nat. Rev. Neurol."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"841","DOI":"10.1016\/j.jalz.2017.06.2266","article-title":"Amyloid \u03b2 concentrations and stable isotope labeling kinetics of human plasma specific to central nervous system amyloidosis","volume":"13","author":"Ovod","year":"2017","journal-title":"Alzheimer\u2019s Dement."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1038\/nature25456","article-title":"High performance plasma amyloid-beta biomarkers for Alzheimer\u2019s disease","volume":"554","author":"Nakamura","year":"2018","journal-title":"Nature"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1186\/s13024-021-00430-x","article-title":"Moving fluid biomarkers for Alzheimer\u2019s disease from research tools to routine clinical diagnostics","volume":"16","author":"Zetterberg","year":"2021","journal-title":"Mol. Neurodegener."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"e20200861","DOI":"10.1084\/jem.20200861","article-title":"Blood plasma phosphorylated-tau isoforms track CNS change in Alzheimer\u2019s disease","volume":"217","author":"Barthelemy","year":"2020","journal-title":"J. Exp. Med."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"515","DOI":"10.3233\/JAD-130380","article-title":"Candidate blood proteome markers of Alzheimer\u2019s disease onset and progression: A systematic review and replication study","volume":"38","author":"Kiddle","year":"2014","journal-title":"J. Alzheimer\u2019s Dis."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"101066","DOI":"10.1016\/j.arr.2020.101066","article-title":"Proteomics as a reliable approach for discovery of blood-based Alzheimer\u2019s disease biomarkers: A systematic review and meta-analysis","volume":"60","author":"Rehiman","year":"2020","journal-title":"Ageing Res. Rev."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"776","DOI":"10.1016\/j.jalz.2019.03.007","article-title":"Inflammatory biomarkers in Alzheimer\u2019s disease plasma","volume":"15","author":"Morgan","year":"2019","journal-title":"Alzheimer\u2019s Dement."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1318","DOI":"10.1001\/archneurol.2012.1282","article-title":"Alzheimer\u2019s Disease Neuroimaging Initiative; Australian Imaging Biomarker and Lifestyle Research Group. Blood-based protein biomarkers for diagnosis of Alzheimer disease","volume":"69","author":"Doecke","year":"2012","journal-title":"Arch. Neurol."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1077","DOI":"10.1001\/archneurol.2010.215","article-title":"A serum protein-based algorithm for the detection of Alzheimer disease","volume":"67","author":"Xiao","year":"2010","journal-title":"Arch. Neurol."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1359","DOI":"10.1038\/nm1653","article-title":"Classification and prediction of clinical Alzheimer\u2019s diagnosis based on plasma signaling proteins","volume":"13","author":"Ray","year":"2007","journal-title":"Nat. Med."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"799","DOI":"10.1016\/j.jalz.2014.05.1749","article-title":"Plasma proteins predict conversion to dementia from prodromal disease","volume":"10","author":"Hye","year":"2014","journal-title":"Alzheimer\u2019s Demen."},{"key":"ref_32","first-page":"3999","article-title":"Serum protein-based profiles as novel biomarkers for the diagnosis of Alzheimer\u2019s disease","volume":"55","author":"Yu","year":"2018","journal-title":"Mol. Neurobiol."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"712545","DOI":"10.3389\/fnagi.2021.712545","article-title":"Plasma Proteomic Biomarkers Relating to Alzheimer\u2019s Disease: A Meta-Analysis Based on Our Own Studies. Front","volume":"13","author":"Shi","year":"2021","journal-title":"Aging Neurosci."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"779","DOI":"10.1007\/s00702-012-0781-3","article-title":"Multidimensional plasma protein separation technique for identification of potential Alzheimer\u2019s disease plasma biomarkers: A pilot study","volume":"119","author":"Henkel","year":"2012","journal-title":"J. Neural. Transm."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"473","DOI":"10.1038\/s43587-021-00064-0","article-title":"Large-scale plasma proteomic analysis identifies proteins and pathways associated with dementia risk","volume":"1","author":"Walker","year":"2021","journal-title":"Nat. Aging"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1186\/s40478-019-0795-2","article-title":"Multiplex proteomics identifies novel CSF and plasma biomarkers of early Alzheimer\u2019s disease","volume":"7","author":"Whelan","year":"2019","journal-title":"Acta Neuropathol. Commun."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1002\/alz.12369","article-title":"Large-scale proteomic profiling identifies a high-performance biomarker panel for Alzheimer\u2019s disease screening and staging","volume":"18","author":"Jiang","year":"2022","journal-title":"Alzheimer\u2019s Demen."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1186\/1477-5956-12-5","article-title":"Plasma protein profiling of mild cognitive impairment and Alzheimer\u2019s disease using iTRAQ quantitative proteomics","volume":"12","author":"Song","year":"2014","journal-title":"Proteome Sci."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1355","DOI":"10.3233\/JAD-141266","article-title":"Plasma protein profiling of mild cognitive impairment and Alzheimer\u2019s disease across two independent cohorts","volume":"43","author":"Muenchhoff","year":"2015","journal-title":"J. Alzheimer\u2019s Dis."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1641","DOI":"10.3233\/JAD-170426","article-title":"Plasma proteomic profiles of cerebrospinal fluid-defined Alzheimer\u2019s disease pathology in older adults","volume":"60","author":"Dayon","year":"2017","journal-title":"J. Alzheimer\u2019s Dis."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1186\/s12014-019-9237-1","article-title":"Deep undepleted human serum proteome profiling toward biomarker discovery for Alzheimer\u2019s disease","volume":"16","author":"Dey","year":"2019","journal-title":"Clin. Proteom."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"101690","DOI":"10.1016\/j.pneurobio.2019.101690","article-title":"Prognostic plasma protein panel for A\u03b2 deposition in the brain in Alzheimer\u2019s disease","volume":"183","author":"Park","year":"2019","journal-title":"Prog. Neurobiol."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"eaau7220","DOI":"10.1126\/sciadv.aau7220","article-title":"A plasma protein classifier for predicting amyloid burden for preclinical Alzheimer\u2019s disease","volume":"5","author":"Ashton","year":"2019","journal-title":"Sci. Adv."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1186\/s13195-020-00634-1","article-title":"Plasma transferrin and hemopexin are associated with altered A\u03b2 uptake and cognitive decline in Alzheimer\u2019s disease pathology","volume":"12","author":"Ashraf","year":"2020","journal-title":"Alzheimer\u2019s Res. Ther."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"349","DOI":"10.3233\/JAD-200110","article-title":"Proteomic profiling of plasma and brain tissue from Alzheimer\u2019s disease patients reveals candidate network of plasma biomarkers","volume":"76","author":"Chen","year":"2020","journal-title":"J. Alzheimer\u2019s Dis."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1327","DOI":"10.3233\/JAD-201318","article-title":"Why Inclusion Matters for Alzheimer\u2019s Disease Biomarker Discovery in Plasma","volume":"79","author":"Khan","year":"2021","journal-title":"J. Alzheimer\u2019s Dis."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"231","DOI":"10.1080\/01616412.2017.1281195","article-title":"Plasma protein profiling for potential biomarkers in the early diagnosis of Alzheimer\u2019s disease","volume":"39","author":"Kitamura","year":"2017","journal-title":"Neurol. Res."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1016\/j.neulet.2018.04.015","article-title":"Proteomics based identification of differential plasma proteins and changes in white matter integrity as markers in early detection of mild cognitive impaired subjects at high risk of Alzheimer\u2019s disease","volume":"676","author":"Kumar","year":"2018","journal-title":"Neurosci. Lett."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"1310","DOI":"10.1001\/archneurol.2012.1070","article-title":"Biomarkers Consortium Alzheimer\u2019s Disease Plasma Proteomics Project. Plasma biomarkers associated with the apolipoprotein E genotype and Alzheimer","volume":"69","author":"Soares","year":"2012","journal-title":"Arch. Neurol."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"1099","DOI":"10.3233\/JAD-2010-101350","article-title":"Proteome-based plasma markers of brain amyloid-\u03b2 deposition in non-demented older individuals","volume":"22","author":"Thambisetty","year":"2010","journal-title":"J. Alzheimer\u2019s Dis."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"549","DOI":"10.3233\/JAD-141149","article-title":"A candidate plasma protein classifier to identify Alzheimer\u2019s disease","volume":"43","author":"Zhao","year":"2015","journal-title":"J. Alzheimer\u2019s Dis."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"633","DOI":"10.1038\/nbt.1546","article-title":"Multi-site assessment of the precision and reproducibility of multiple reaction monitoring-based measurements of proteins in plasma","volume":"27","author":"Addona","year":"2009","journal-title":"Nat. Biotech."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"3634","DOI":"10.1039\/C9AN01893J","article-title":"Determination of the concentration range for 267 proteins from 21 lots of commercial human plasma using highly multiplexed multiple reaction monitoring mass spectrometry","volume":"145","author":"Gaither","year":"2020","journal-title":"Analyst"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"e9356","DOI":"10.15252\/msb.20199356","article-title":"Proteome profiling in cerebrospinal fluid reveals novel biomarkers of Alzheimer\u2019s disease","volume":"16","author":"Bader","year":"2020","journal-title":"Mol. Syst. Biol."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1038\/s41551-021-00819-5","article-title":"Amelioration of Alzheimer\u2019s disease pathology by mitophagy inducers identified via machine learning and a cross-species workflow","volume":"6","author":"Xie","year":"2022","journal-title":"Nat. Biomed. Eng."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"4292","DOI":"10.1021\/acs.jproteome.1c00249","article-title":"Performance assessment of a 125 human plasma peptide mixture stored at room temperature for multiple reaction monitoring-mass spectrometry","volume":"20","author":"Gaither","year":"2021","journal-title":"J. Proteome Res."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"569","DOI":"10.1159\/000322092","article-title":"Systemic inflammation is associated with MCI and its subtypes: The Sydney Memory and Aging Study","volume":"30","author":"Trollor","year":"2010","journal-title":"Dement. Geriatr. Cogn. Disord."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"2403","DOI":"10.18632\/aging.101924","article-title":"Identification of inflammatory and vascular markers associated with mild cognitive impairment","volume":"11","author":"Shen","year":"2019","journal-title":"Aging"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"69","DOI":"10.4103\/1673-5374.125332","article-title":"A non-invasive, rapid method to genotype late-onset Alzheimer\u2019s disease-related apolipoprotein E gene polymorphisms","volume":"9","author":"Yi","year":"2014","journal-title":"Neural Regen. Res."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1016\/0022-3956(75)90026-6","article-title":"A practical method for grading the cognitive state of patients for the children","volume":"12","author":"Folstein","year":"1975","journal-title":"J. Psychiatr. Res."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1016\/0278-2626(92)90112-Y","article-title":"Quantitative and qualitative analyses of clock drawings in Alzheimer\u2019s and Huntington\u2019s disease","volume":"18","author":"Rouleau","year":"1992","journal-title":"Brain Cogn."},{"key":"ref_62","unstructured":"Kaplan, E., Goodglass, H., and Weintraub, S. (2001). Boston Naming Test, Pro-Ed."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"1278","DOI":"10.1073\/pnas.70.4.1278","article-title":"Neuropsychological studies in the USSR. A review (part II)","volume":"70","author":"Luria","year":"1973","journal-title":"Proc. Natl Acad. Sci. USA"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"190","DOI":"10.1002\/1097-4679(199003)46:2<190::AID-JCLP2270460211>3.0.CO;2-M","article-title":"Luria Memory Words Test and Wechsler Memory Scale: Comparison of utility in discriminating neurologically impaired from controls","volume":"46","author":"Altepeter","year":"1990","journal-title":"J. Clin. Psychol."},{"key":"ref_65","unstructured":"Mattis, S. (1988). Dementia Rating Scale: Professional Manual, Psychological Assessment Resources."},{"key":"ref_66","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_67","unstructured":"World Health Organization (1992). The ICD-10 Classification of Mental and Behavioural Disorders: Clinical Descriptions and Diagnostic Guidelines."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"234","DOI":"10.17925\/ENR.2011.06.04.234","article-title":"New diagnostic criteria for the behavioural variant of frontotemporal dementia","volume":"6","author":"Pijnenburg","year":"2011","journal-title":"Eur. Neurol. Rev."},{"key":"ref_69","first-page":"24","article-title":"Consensus in mild cognitive impairment. Research and practice in Alzheimers disease","volume":"10","author":"Peterson","year":"2005","journal-title":"EADS ADCS Jt. Meet"},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"1136","DOI":"10.1176\/ajp.139.9.1136","article-title":"The Global Deterioration Scale for assessment of primary degenerative dementia","volume":"139","author":"Reisberg","year":"1982","journal-title":"Am. J. Psychiatry"},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"353","DOI":"10.1007\/978-1-0716-1024-4_25","article-title":"Detailed Method for Performing the ExSTA Approach in Quantitative Bottom-Up Plasma Proteomics","volume":"2228","author":"Percy","year":"2021","journal-title":"Methods Mol. Biol."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"1600180","DOI":"10.1002\/prca.201600180","article-title":"ExSTA: External standard addition method for accurate high-throughput quantitation in targeted proteomics experiments","volume":"12","author":"Mohammed","year":"2018","journal-title":"Proteom. Clin. Appl."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"966","DOI":"10.1093\/bioinformatics\/btq054","article-title":"Skyline: An open source document editor for creating and analyzing targeted proteomics experiments","volume":"26","author":"MacLean","year":"2010","journal-title":"Bioinformatics"},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"2182","DOI":"10.1007\/s13361-018-2028-5","article-title":"Using skyline to analyze data-containing liquid chromatography, ion mobility spectrometry, and mass spectrometry dimensions","volume":"29","author":"MacLean","year":"2018","journal-title":"J. Am. Soc. Mass Spectrom."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1038\/s41592-019-0686-2","article-title":"SciPy 1.0: Fundamental algorithms for scientific computing in Python","volume":"17","author":"Virtanen","year":"2020","journal-title":"Nat. Methods"},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"3021","DOI":"10.21105\/joss.03021","article-title":"Seaborn: Statistical data visualization","volume":"6","author":"Waskom","year":"2021","journal-title":"J. Open Source Softw."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1109\/MCSE.2007.55","article-title":"Matplotlib: A 2D graphics environment","volume":"9","author":"Hunter","year":"2007","journal-title":"Comput. Sci. Eng."},{"key":"ref_78","unstructured":"McKinney, W. (July, January 28). Data structures for statistical computing in python. Proceedings of the 9th Python in Science Conference, Austin, TX, USA."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"731","DOI":"10.1038\/nmeth.3901","article-title":"The Perseus computational platform for comprehensive analysis of (prote) omics data","volume":"13","author":"Tyanova","year":"2016","journal-title":"Nat. Methods"},{"key":"ref_80","first-page":"2825","article-title":"Scikit-learn: Machine learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Machine Learn. Res."},{"key":"ref_81","doi-asserted-by":"crossref","unstructured":"Chen, T., and Guestrin, C. (2016, January 13\u201317). Xgboost: A scalable tree boosting system. Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA.","DOI":"10.1145\/2939672.2939785"}],"container-title":["International Journal of Molecular Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1422-0067\/23\/14\/7907\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:53:02Z","timestamp":1760140382000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1422-0067\/23\/14\/7907"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,7,18]]},"references-count":81,"journal-issue":{"issue":"14","published-online":{"date-parts":[[2022,7]]}},"alternative-id":["ijms23147907"],"URL":"https:\/\/doi.org\/10.3390\/ijms23147907","relation":{},"ISSN":["1422-0067"],"issn-type":[{"value":"1422-0067","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,7,18]]}}}