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Machine intelligence-driven classification of cancer patients-derived extracellular vesicles using fluorescence correlation spectroscopy: results from a pilot study |
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| Authors: | Uthamacumaran Abicumaran Abdouh Mohamed Sengupta Kinshuk Gao Zu-hua Forte Stefano Tsering Thupten Burnier Julia V. Arena Goffredo |
| Affiliation: | 1.Department of Physics (Alumni), Concordia University, Montreal, QC, Canada ;2.McGill Genome Center (Majewski Lab), 740 Dr Penfield Ave, Montreal, QC, H3A 0G1, Canada ;3.Cancer Research Program, Research Institute of the McGill University Health Centre, 1001 Decarie Boulevard, Montreal, QC, H4A 3J1, Canada ;4.The Henry C. Witelson Ocular Pathology Laboratory, McGill University, Montreal, QC, Canada ;5.Microsoft Research and Development, New Delhi, India ;6.Department of Pathology and Laboratory Medicine, University of British Columbia, G105-2211 Wesbrook Mall, Vancouver, BC, Canada ;7.IOM Ricera, Via Penninazzo 11, 95029, Viagrande, Italy ;8.Gerald Bronfman Department of Oncology, McGill University, Montreal, QC, Canada ;9.Experimental Pathology Unit, Department of Pathology, McGill University, Montreal, QC, Canada ;10.Istituto Mediterraneo Di Oncologia, Viagrande, Italy ;11.Department of Surgery, McGill University, Montreal, QC, Canada ;12.Fondazione Gemelli-Giglio, Contrada Pollastra, Cefalù, Italy ; |
| Abstract: | Neural Computing and Applications - Patient-derived extracellular vesicles (EVs) that contains a complex biological cargo is a valuable source of liquid-biopsy diagnostics to aid in early... |
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