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Learning cell migration mechanisms using machine learning
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DOI：10.1016/j.rineng.2024.102295.png)
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Abstract
En 中文
Cell movement is known to be fundamental to many processes that take part in the overall functioning of life itself. Regeneration of damaged tissue, tumor growth or the creation of life itself through embryogenesis rely mainly on the individual and collective movement of cells. Due to the importance of such phenomena, increasing research activity has been dedicated to them, but many are the questions that still remain, specially when focusing on the movement of clusters and networks of cells as a whole. We present a new approach to take advantage of Machine Learning tools (Artificial Neural Networks) in order to predict the position and velocity of a cell in a certain moment in time, given the properties of said cell and the environment surrounding it. The results show a high level of accuracy for the analyzed video sequence, which implies that the studied properties greatly influence the decisions taken by cells regarding its movement.
Keywords:
Cell migration
Artificial intelligence
Artificial neural networks
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