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Cited by in F6Publishing
For: Uthamacumaran A, Elouatik S, Abdouh M, Berteau-rainville M, Gao Z, Arena G. Machine learning characterization of cancer patients-derived extracellular vesicles using vibrational spectroscopies: results from a pilot study. Appl Intell. [DOI: 10.1007/s10489-022-03203-1] [Cited by in Crossref: 1] [Cited by in F6Publishing: 3] [Article Influence: 1.0] [Reference Citation Analysis]
Number Citing Articles
1 Ye S, You Q, Song S, Wang H, Wang C, Zhu L, Yang Y. Nanostructures and Nanotechnologies for the Detection of Extracellular Vesicle. Adv Biol (Weinh) 2023;7:e2200201. [PMID: 36394211 DOI: 10.1002/adbi.202200201] [Reference Citation Analysis]
2 Wong LW, Mak SH, Goh BH, Lee WL. The Convergence of FTIR and EVs: Emergence Strategy for Non-Invasive Cancer Markers Discovery. Diagnostics (Basel) 2022;13. [PMID: 36611313 DOI: 10.3390/diagnostics13010022] [Reference Citation Analysis]
3 Uthamacumaran A, Abdouh M, Sengupta K, Gao Z, Forte S, Tsering T, Burnier JV, Arena G. Machine intelligence-driven classification of cancer patients-derived extracellular vesicles using fluorescence correlation spectroscopy: results from a pilot study. Neural Comput & Applic 2022. [DOI: 10.1007/s00521-022-08113-4] [Reference Citation Analysis]
4 Cansever Mutlu E, Kaya M, Küçük I, Ben-Nissan B, Stamboulis A. Exosome Structures Supported by Machine Learning Can Be Used as a Promising Diagnostic Tool. Materials (Basel) 2022;15. [PMID: 36431454 DOI: 10.3390/ma15227967] [Reference Citation Analysis]
5 Lu H, Lin X, Wang X, Du P. Spike-train level supervised learning algorithm based on bidirectional modification for liquid state machines. Appl Intell. [DOI: 10.1007/s10489-022-04152-5] [Reference Citation Analysis]
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