Machine learning for stem cell differentiation and proliferation classification on electrical impedance spectroscopy
Electrical impedance spectroscopy (EIS) measurements on cells is a proven method to assess stem cell proliferation and differentiation. Cell regenerative medicine (CRM) is an emerging field where the need to develop and deploy stem cell assessment techniques is paramount as experimental treatments r...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Sciendo
2019-12-01
|
| Series: | Journal of Electrical Bioimpedance |
| Subjects: | |
| Online Access: | https://doi.org/10.2478/joeb-2019-0018 |
| _version_ | 1818011267204382720 |
|---|---|
| author | Cunha André B. Hou Jie Schuelke Christin |
| author_facet | Cunha André B. Hou Jie Schuelke Christin |
| author_sort | Cunha André B. |
| collection | DOAJ |
| description | Electrical impedance spectroscopy (EIS) measurements on cells is a proven method to assess stem cell proliferation and differentiation. Cell regenerative medicine (CRM) is an emerging field where the need to develop and deploy stem cell assessment techniques is paramount as experimental treatments reach pre-clinical and clinical stages. However, EIS measurements on cells is a method requiring extensive post-processing and analysis. As a contribution to address this concern, we developed three machine learning models for three different stem cell lines able to classify the measured data as proliferation or differentiation laying the stone for future studies on using machine learning to profile EIS measurements on stem cells spectra. |
| first_indexed | 2024-04-14T06:05:40Z |
| format | Article |
| id | doaj.art-6dc86695b67a42899b533b6e7f6bb5d1 |
| institution | Directory Open Access Journal |
| issn | 1891-5469 |
| language | English |
| last_indexed | 2024-04-14T06:05:40Z |
| publishDate | 2019-12-01 |
| publisher | Sciendo |
| record_format | Article |
| series | Journal of Electrical Bioimpedance |
| spelling | doaj.art-6dc86695b67a42899b533b6e7f6bb5d12022-12-22T02:08:32ZengSciendoJournal of Electrical Bioimpedance1891-54692019-12-0110112413210.2478/joeb-2019-0018Machine learning for stem cell differentiation and proliferation classification on electrical impedance spectroscopyCunha André B.0Hou Jie1Schuelke Christin2Department of Physics, University of Oslo, Oslo, NorwayDepartment of Physics, University of Oslo, Oslo, NorwayDepartment of Physics, University of Oslo, Oslo, NorwayElectrical impedance spectroscopy (EIS) measurements on cells is a proven method to assess stem cell proliferation and differentiation. Cell regenerative medicine (CRM) is an emerging field where the need to develop and deploy stem cell assessment techniques is paramount as experimental treatments reach pre-clinical and clinical stages. However, EIS measurements on cells is a method requiring extensive post-processing and analysis. As a contribution to address this concern, we developed three machine learning models for three different stem cell lines able to classify the measured data as proliferation or differentiation laying the stone for future studies on using machine learning to profile EIS measurements on stem cells spectra.https://doi.org/10.2478/joeb-2019-0018machine learningelectrical impedance spectroscopystem cellsdifferentiationproliferationrecurrent neural networkslong term short term memory |
| spellingShingle | Cunha André B. Hou Jie Schuelke Christin Machine learning for stem cell differentiation and proliferation classification on electrical impedance spectroscopy Journal of Electrical Bioimpedance machine learning electrical impedance spectroscopy stem cells differentiation proliferation recurrent neural networks long term short term memory |
| title | Machine learning for stem cell differentiation and proliferation classification on electrical impedance spectroscopy |
| title_full | Machine learning for stem cell differentiation and proliferation classification on electrical impedance spectroscopy |
| title_fullStr | Machine learning for stem cell differentiation and proliferation classification on electrical impedance spectroscopy |
| title_full_unstemmed | Machine learning for stem cell differentiation and proliferation classification on electrical impedance spectroscopy |
| title_short | Machine learning for stem cell differentiation and proliferation classification on electrical impedance spectroscopy |
| title_sort | machine learning for stem cell differentiation and proliferation classification on electrical impedance spectroscopy |
| topic | machine learning electrical impedance spectroscopy stem cells differentiation proliferation recurrent neural networks long term short term memory |
| url | https://doi.org/10.2478/joeb-2019-0018 |
| work_keys_str_mv | AT cunhaandreb machinelearningforstemcelldifferentiationandproliferationclassificationonelectricalimpedancespectroscopy AT houjie machinelearningforstemcelldifferentiationandproliferationclassificationonelectricalimpedancespectroscopy AT schuelkechristin machinelearningforstemcelldifferentiationandproliferationclassificationonelectricalimpedancespectroscopy |