Single cell classification using statistical learning on mechanical properties measured by mems tweezers

Autor: Ahmadian, Bahram, Mbujamba, Deborah, Gerbedoen, Jean Claude, Kumemura, Momoko, Fujita, Hiroyuki, Collard, Dominique, Dabo-Niang, Sophie, Lagadec, Chann, Tarhan, Mehmet

Publikováno v: IEEE 35th International Conference on Micro Electro Mechanical Systems Conference (MEMS 2022)
IEEE 35th International Conference on Micro Electro Mechanical Systems Conference (MEMS 2022), Jan 2022, Tokyo, Japan. pp.317-320, ⟨10.1109/MEMS51670.2022.9699466⟩
2022 IEEE 35th International Conference on Micro Electro Mechanical Systems Conference (MEMS)
2022 IEEE 35th International Conference on Micro Electro Mechanical Systems Conference (MEMS), Jan 2022, Tokyo, Japan. pp.317-320, ⟨10.1109/MEMS51670.2022.9699466⟩

International audience; Cell population is heterogenous and so presents a wide range of properties as metastatic potential. But using rare cells for clinical applications requires precise classification of individual cells. Here, we propose a multi-p

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1d8f29ef61c9918f21d724cc961de877
https://inria.hal.science/hal-03528082/document