<abstract xmlns="http://www.w3.org/1999/xhtml"><p>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.</p></abstract>

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.

Machine learning for stem cell differentiation and proliferation classification on electrical impedance spectroscopy

Journal of Electrical Bioimpedance

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Electrical impedance spectroscopy (EIS) measurements on cells is a proven method to assess stem cell proliferation and differentiation. Cell regenerative...

Machine learning for stem cell differentiation and proliferation classification on electrical impedance spectroscopy

Online veröffentlicht: 31. Dez. 2019

Seitenbereich: 124 - 132

Eingereicht: 01. Dez. 2019

DOI: https://doi.org/10.2478/joeb-2019-0018

Schlüsselwörter
machine learning, electrical impedance spectroscopy, stem cells, differentiation, proliferation, recurrent neural networks, long term short term memory

© 2019 André B. Cunha et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Machine learning for stem cell differentiation and proliferation classification on electrical impedance spectroscopy

Online veröffentlicht: 31. Dez. 2019

Seitenbereich: 124 - 132

Eingereicht: 01. Dez. 2019

DOI: https://doi.org/10.2478/joeb-2019-0018

Schlüsselwörtermachine learning, electrical impedance spectroscopy, stem cells, differentiation, proliferation, recurrent neural networks, long term short term memory

© 2019 André B. Cunha et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Schlüsselwörter
machine learning, electrical impedance spectroscopy, stem cells, differentiation, proliferation, recurrent neural networks, long term short term memory