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Description and comparision of LSTM and Gaussian Process (GP) to predict cellular migration under an electric field.

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Prediction of Cellular Migration under Galvanotaxis

Description and comparison of LSTM and Gaussian Process (GP) to predict cellular migration under an electric field.

The LSTM code is adapted from Sargent et al. (2022), which can be found here: https://github.com/Gomez-Lab/galvanotaxis_cells and the training data is from Arocena et al (2017) found here: https://datadryad.org/stash/dataset/doi:10.5061/dryad.53512.

References

Sargent, B., Jafari, M., Marquez, G., et al. A machine learning-based model accurately predicts cellular response to electric fields in multiple cell types. Sci Rep 12, 9912 (2022). https://doi.org/10.1038/s41598-022-13925-4

Arocena, M., Rajnicek, A. M., & Collinson, J. M. (2017). Requirement of Pax6 for the integration of guidance cues in cell migration. Royal Society Open Science, 4(10), 170625. https://doi.org/10.1098/rsos.170625

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Description and comparision of LSTM and Gaussian Process (GP) to predict cellular migration under an electric field.

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