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For: Gupta Y, Lama RK, Kwon GR; Alzheimer's Disease Neuroimaging Initiative. Prediction and Classification of Alzheimer's Disease Based on Combined Features From Apolipoprotein-E Genotype, Cerebrospinal Fluid, MR, and FDG-PET Imaging Biomarkers. Front Comput Neurosci 2019;13:72. [PMID: 31680923 DOI: 10.3389/fncom.2019.00072] [Cited by in Crossref: 31] [Cited by in F6Publishing: 21] [Article Influence: 10.3] [Reference Citation Analysis]
Number Citing Articles
1 Xia J, Sun L, Xu S, Xiang Q, Zhao J, Xiong W, Xu Y, Chu S. A Model Using Support Vector Machines Recursive Feature Elimination (SVM-RFE) Algorithm to Classify Whether COPD Patients Have Been Continuously Managed According to GOLD Guidelines. Int J Chron Obstruct Pulmon Dis 2020;15:2779-86. [PMID: 33177815 DOI: 10.2147/COPD.S271237] [Cited by in Crossref: 5] [Cited by in F6Publishing: 1] [Article Influence: 2.5] [Reference Citation Analysis]
2 Díaz-álvarez J, Matias-guiu JA, Cabrera-martín MN, Pytel V, Segovia-ríos I, García-gutiérrez F, Hernández-lorenzo L, Matias-guiu J, Carreras JL, Ayala JL; Alzheimer’s Disease Neuroimaging Initiative. Genetic Algorithms for Optimized Diagnosis of Alzheimer’s Disease and Frontotemporal Dementia Using Fluorodeoxyglucose Positron Emission Tomography Imaging. Front Aging Neurosci 2022;13:708932. [DOI: 10.3389/fnagi.2021.708932] [Reference Citation Analysis]
3 Mora Pinzon M, Krainer J, LeCaire T, Houston S, Green-Harris G, Norris N, Barnes S, Clark LR, Gleason CE, Hermann BP, Ramon H, Buckingham W, Chin NA, Asthana S, Johnson SC, Walaszek A, Carlsson CM. The Wisconsin Alzheimer's Institute Dementia Diagnostic Clinic Network: A community of practice to improve dementia care. J Am Geriatr Soc 2022. [PMID: 35362093 DOI: 10.1111/jgs.17768] [Reference Citation Analysis]
4 Perez-Gonzalez J, Jiménez-Ángeles L, Rojas Saavedra K, Barbará Morales E, Medina-Bañuelos V. Mild cognitive impairment classification using combined structural and diffusion imaging biomarkers. Phys Med Biol 2021;66. [PMID: 34167090 DOI: 10.1088/1361-6560/ac0e77] [Reference Citation Analysis]
5 Xue W, Li J, Fu K, Teng W. Differential Expression of mRNAs in Peripheral Blood Related to Prodrome and Progression of Alzheimer's Disease. Biomed Res Int 2020;2020:4505720. [PMID: 33204697 DOI: 10.1155/2020/4505720] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 0.5] [Reference Citation Analysis]
6 Wabik A, Trypka E, Bladowska J, Statkiewicz M, Sąsiadek M, Zimny A. Comparison of dynamic susceptibility contrast enhanced MR and FDG-PET brain studies in patients with Alzheimer’s disease and amnestic mild cognitive impairment. J Transl Med 2022;20. [DOI: 10.1186/s12967-022-03464-x] [Reference Citation Analysis]
7 P J, G JS, K S S, S RW. Clinical decision support system for early detection of Alzheimer's disease using an enhanced gradient boosted decision tree classifier. Health Informatics J 2022;28:14604582221082868. [PMID: 35350906 DOI: 10.1177/14604582221082868] [Reference Citation Analysis]
8 Pyatigorskaya N, Habert MO, Rozenblum L. Contribution of PET-MRI in brain diseases in clinical practice. Curr Opin Neurol 2020;33:430-8. [PMID: 32657884 DOI: 10.1097/WCO.0000000000000841] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
9 Gupta Y, Kim JI, Kim BC, Kwon GR. Classification and Graphical Analysis of Alzheimer's Disease and Its Prodromal Stage Using Multimodal Features From Structural, Diffusion, and Functional Neuroimaging Data and the APOE Genotype. Front Aging Neurosci 2020;12:238. [PMID: 32848713 DOI: 10.3389/fnagi.2020.00238] [Cited by in Crossref: 4] [Cited by in F6Publishing: 3] [Article Influence: 2.0] [Reference Citation Analysis]
10 Liu X, He C, Fan D, Zang F, Zhu Y, Zhang H, Zhang Z, Zhang H, Xie C. Alterations of core structural network connectome associated with suicidal ideation in major depressive disorder patients. Transl Psychiatry 2021;11:243. [PMID: 33895787 DOI: 10.1038/s41398-021-01353-3] [Reference Citation Analysis]
11 Sendi MSE, Zendehrouh E, Fu Z, Liu J, Du Y, Mormino E, Salat DH, Calhoun VD, Miller RL. Disrupted Dynamic Functional Network Connectivity Among Cognitive Control Networks in the Progression of Alzheimer's Disease. Brain Connect 2021. [PMID: 34102870 DOI: 10.1089/brain.2020.0847] [Reference Citation Analysis]
12 Gavrilova SI, Alvarez A. Cerebrolysin in the therapy of mild cognitive impairment and dementia due to Alzheimer's disease: 30 years of clinical use. Med Res Rev 2021;41:2775-803. [PMID: 32808294 DOI: 10.1002/med.21722] [Cited by in Crossref: 5] [Cited by in F6Publishing: 5] [Article Influence: 2.5] [Reference Citation Analysis]
13 Ansingkar NP, Patil RB, Deshmukh PD. An efficient multi class Alzheimer detection using hybrid equilibrium optimizer with capsule auto encoder. Multimed Tools Appl. [DOI: 10.1007/s11042-021-11786-z] [Reference Citation Analysis]
14 Burgos N, Colliot O. Machine learning for classification and prediction of brain diseases: recent advances and upcoming challenges. Current Opinion in Neurology 2020;33:439-50. [DOI: 10.1097/wco.0000000000000838] [Cited by in Crossref: 6] [Cited by in F6Publishing: 2] [Article Influence: 3.0] [Reference Citation Analysis]
15 Gaubert S, Houot M, Raimondo F, Ansart M, Corsi MC, Naccache L, Sitt JD, Habert MO, Dubois B, De Vico Fallani F, Durrleman S, Epelbaum S; INSIGHT-preAD study group. A machine learning approach to screen for preclinical Alzheimer's disease. Neurobiol Aging 2021;105:205-16. [PMID: 34102381 DOI: 10.1016/j.neurobiolaging.2021.04.024] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
16 Grueso S, Viejo-Sobera R. Machine learning methods for predicting progression from mild cognitive impairment to Alzheimer's disease dementia: a systematic review. Alzheimers Res Ther 2021;13:162. [PMID: 34583745 DOI: 10.1186/s13195-021-00900-w] [Reference Citation Analysis]
17 Mirzaei G, Adeli H. Machine learning techniques for diagnosis of alzheimer disease, mild cognitive disorder, and other types of dementia. Biomedical Signal Processing and Control 2022;72:103293. [DOI: 10.1016/j.bspc.2021.103293] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
18 Jitsuishi T, Yamaguchi A. Searching for optimal machine learning model to classify mild cognitive impairment (MCI) subtypes using multimodal MRI data. Sci Rep 2022;12:4284. [PMID: 35277565 DOI: 10.1038/s41598-022-08231-y] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
19 Drouin SM, Mcfall GP, Potvin O, Bellec P, Masellis M, Duchesne S, Dixon RA, Resnick S; for the Alzheimer’s Disease Neuroimaging Initiative. Data-Driven Analyses of Longitudinal Hippocampal Imaging Trajectories: Discrimination and Biomarker Prediction of Change Classes. JAD 2022. [DOI: 10.3233/jad-215289] [Reference Citation Analysis]
20 Mohan A, Sun Z, Ghosh S, Li Y, Sathe S, Hu J, Sampaio C. A Machine-Learning Derived Huntington's Disease Progression Model: Insights for Clinical Trial Design. Mov Disord 2021. [PMID: 34870344 DOI: 10.1002/mds.28866] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
21 Bermejo-Pareja F, Contador I, Del Ser T, Olazarán J, Llamas-Velasco S, Vega S, Benito-León J. Predementia constructs: Mild cognitive impairment or mild neurocognitive disorder? A narrative review. Int J Geriatr Psychiatry 2020. [PMID: 33340379 DOI: 10.1002/gps.5474] [Reference Citation Analysis]
22 Skolariki K, Terrera GM, Danso SO. Predictive models for mild cognitive impairment to Alzheimer's disease conversion. Neural Regen Res 2021;16:1766-7. [PMID: 33510068 DOI: 10.4103/1673-5374.306071] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
23 Balea-fernandez FJ, Martinez-vega B, Ortega S, Fabelo H, Leon R, Callico GM, Bibao-sieyro C. Analysis of Risk Factors in Dementia Through Machine Learning. JAD 2021;79:845-61. [DOI: 10.3233/jad-200955] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
24 Amini M, Pedram MM, Moradi A, Jamshidi M, Ouchani M. Single and Combined Neuroimaging Techniques for Alzheimer's Disease Detection. Comput Intell Neurosci 2021;2021:9523039. [PMID: 34335726 DOI: 10.1155/2021/9523039] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
25 Cardoso R, Lemos C, Oliveiros B, Almeida MR, Baldeiras I, Pereira CF, Santos A, Duro D, Vieira D, Santana I, Oliveira CR. APOEɛ4-TOMM40L Haplotype Increases the Risk of Mild Cognitive Impairment Conversion to Alzheimer's Disease. J Alzheimers Dis 2020;78:587-601. [PMID: 33016906 DOI: 10.3233/JAD-200556] [Reference Citation Analysis]
26 Reel PS, Reel S, Pearson E, Trucco E, Jefferson E. Using machine learning approaches for multi-omics data analysis: A review. Biotechnol Adv 2021;49:107739. [PMID: 33794304 DOI: 10.1016/j.biotechadv.2021.107739] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 4.0] [Reference Citation Analysis]
27 Toshkhujaev S, Lee KH, Choi KY, Lee JJ, Kwon GR, Gupta Y, Lama RK. Classification of Alzheimer's Disease and Mild Cognitive Impairment Based on Cortical and Subcortical Features from MRI T1 Brain Images Utilizing Four Different Types of Datasets. J Healthc Eng 2020;2020:3743171. [PMID: 32952988 DOI: 10.1155/2020/3743171] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
28 El-sappagh S, Saleh H, Ali F, Amer E, Abuhmed T. Two-stage deep learning model for Alzheimer’s disease detection and prediction of the mild cognitive impairment time. Neural Comput & Applic. [DOI: 10.1007/s00521-022-07263-9] [Reference Citation Analysis]
29 Maiuolo J, Gliozzi M, Musolino V, Carresi C, Scarano F, Nucera S, Scicchitano M, Oppedisano F, Bosco F, Ruga S, Zito MC, Macri R, Palma E, Muscoli C, Mollace V. The Contribution of Gut Microbiota-Brain Axis in the Development of Brain Disorders. Front Neurosci 2021;15:616883. [PMID: 33833660 DOI: 10.3389/fnins.2021.616883] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 3.0] [Reference Citation Analysis]
30 Sedghizadeh MJ, Hojjati H, Ezzatdoost K, Aghajan H, Vahabi Z, Tarighatnia H. Olfactory response as a marker for Alzheimer's disease: Evidence from perceptual and frontal lobe oscillation coherence deficit. PLoS One 2020;15:e0243535. [PMID: 33320870 DOI: 10.1371/journal.pone.0243535] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
31 Zeng HM, Han HB, Zhang QF, Bai H. Application of modern neuroimaging technology in the diagnosis and study of Alzheimer's disease. Neural Regen Res 2021;16:73-9. [PMID: 32788450 DOI: 10.4103/1673-5374.286957] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 3.0] [Reference Citation Analysis]
32 Li W, Zhao Z, Liu M, Yan S, An Y, Qiao L, Wang G, Qi Z, Lu J. Multimodal Classification of Alzheimer's Disease and Amnestic Mild Cognitive Impairment: Integrated 18F-FDG PET and DTI Study. J Alzheimers Dis 2021. [PMID: 34897092 DOI: 10.3233/JAD-215338] [Reference Citation Analysis]
33 e Souza Rodrigues B, Martins Floriano C, Pereira V, Costa Roboredo M. An algorithm to elicitate ELECTRE II, III and IV parameters. DTA 2020;55:82-96. [DOI: 10.1108/dta-07-2020-0161] [Cited by in Crossref: 1] [Article Influence: 0.5] [Reference Citation Analysis]
34 Moss DE. Improving Anti-Neurodegenerative Benefits of Acetylcholinesterase Inhibitors in Alzheimer's Disease: Are Irreversible Inhibitors the Future? Int J Mol Sci 2020;21:E3438. [PMID: 32414155 DOI: 10.3390/ijms21103438] [Cited by in Crossref: 12] [Cited by in F6Publishing: 8] [Article Influence: 6.0] [Reference Citation Analysis]
35 Reiss AB, Glass AD, Wisniewski T, Wolozin B, Gomolin IH, Pinkhasov A, De Leon J, Stecker MM. Alzheimer's disease: many failed trials, so where do we go from here? J Investig Med 2020;68:1135-40. [PMID: 32699179 DOI: 10.1136/jim-2020-001297] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.5] [Reference Citation Analysis]