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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: 1] [Article Influence: 1.0] [Reference Citation Analysis]
Number Citing Articles
1 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] [Reference Citation Analysis]
2 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]
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 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]
5 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: 1] [Article Influence: 1.0] [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 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]
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: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
9 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]
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 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] [Reference Citation Analysis]