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For: Chang CH, Lin CH, Lane HY. Machine Learning and Novel Biomarkers for the Diagnosis of Alzheimer's Disease. Int J Mol Sci 2021;22:2761. [PMID: 33803217 DOI: 10.3390/ijms22052761] [Cited by in Crossref: 1] [Cited by in F6Publishing: 12] [Article Influence: 1.0] [Reference Citation Analysis]
Number Citing Articles
1 Sabharwal R, Miah SJ. An intelligent literature review: adopting inductive approach to define machine learning applications in the clinical domain. J Big Data 2022;9. [DOI: 10.1186/s40537-022-00605-3] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
2 Huang J, Lv P, Lian Y, Zhang M, Ge X, Li S, Pan Y, Zhao J, Xu Y, Tang H, Li N, Zhang Z. Construction of machine learning tools to predict threatened miscarriage in the first trimester based on AEA, progesterone and β-hCG in China: a multicentre, observational, case-control study. BMC Pregnancy Childbirth 2022;22. [DOI: 10.1186/s12884-022-05025-y] [Reference Citation Analysis]
3 Chu SS, Nguyen HA, Zhang J, Tabassum S, Cao H. Towards Multiplexed and Multimodal Biosensor Platforms in Real-Time Monitoring of Metabolic Disorders. Sensors 2022;22:5200. [DOI: 10.3390/s22145200] [Reference Citation Analysis]
4 de Fátima Cobre A, Surek M, Stremel DP, Fachi MM, Lobo Borba HH, Tonin FS, Pontarolo R. Diagnosis and prognosis of COVID-19 employing analysis of patients' plasma and serum via LC-MS and machine learning. Computers in Biology and Medicine 2022;146:105659. [DOI: 10.1016/j.compbiomed.2022.105659] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
5 Hawksworth J, Fernández E, Gevaert K. A new generation of AD biomarkers: 2019 to 2021. Ageing Res Rev 2022;79:101654. [PMID: 35636691 DOI: 10.1016/j.arr.2022.101654] [Reference Citation Analysis]
6 Qin Q, Gu Z, Li F, Pan Y, Zhang T, Fang Y, Zhang L. A Diagnostic Model for Alzheimer’s Disease Based on Blood Levels of Autophagy-Related Genes. Front Aging Neurosci 2022;14:881890. [DOI: 10.3389/fnagi.2022.881890] [Reference Citation Analysis]
7 Wiatrak B, Balon K, Jawień P, Bednarz D, Jęśkowiak I, Szeląg A. The Role of the Microbiota-Gut-Brain Axis in the Development of Alzheimer's Disease. Int J Mol Sci 2022;23:4862. [PMID: 35563253 DOI: 10.3390/ijms23094862] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
8 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: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
9 Li Z, Jiang X, Wang Y, Kim Y. Applied machine learning in Alzheimer's disease research: omics, imaging, and clinical data. Emerg Top Life Sci 2021;5:765-77. [PMID: 34881778 DOI: 10.1042/ETLS20210249] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
10 Celaya-Padilla JM, Villagrana-Bañuelos KE, Oropeza-Valdez JJ, Monárrez-Espino J, Castañeda-Delgado JE, Oostdam ASH, Fernández-Ruiz JC, Ochoa-González F, Borrego JC, Enciso-Moreno JA, López JA, López-Hernández Y, Galván-Tejada CE. Kynurenine and Hemoglobin as Sex-Specific Variables in COVID-19 Patients: A Machine Learning and Genetic Algorithms Approach. Diagnostics (Basel) 2021;11:2197. [PMID: 34943434 DOI: 10.3390/diagnostics11122197] [Reference Citation Analysis]
11 Brogi S, Calderone V. Artificial Intelligence in Translational Medicine. IJTM 2021;1:223-85. [DOI: 10.3390/ijtm1030016] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
12 Fabrizio C, Termine A, Caltagirone C, Sancesario G. Artificial Intelligence for Alzheimer's Disease: Promise or Challenge? Diagnostics (Basel) 2021;11:1473. [PMID: 34441407 DOI: 10.3390/diagnostics11081473] [Cited by in Crossref: 4] [Cited by in F6Publishing: 6] [Article Influence: 4.0] [Reference Citation Analysis]