Mathematical Modelling of NHU Cell Dynamics and Proliferation Using Interpretable Machine Learning
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- @InProceedings{Mokhtar:2024:ICDS,
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author = "N. F. M. Mokhtar and M. I. S. Amir",
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title = "Mathematical Modelling of {NHU} Cell Dynamics and
Proliferation Using Interpretable Machine Learning",
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booktitle = "2024 Sixth International Conference on Intelligent
Computing in Data Sciences (ICDS)",
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year = "2024",
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month = oct,
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keywords = "genetic algorithms, genetic programming, Computational
modelling, Machine learning, Predictive models, Feature
extraction, Mathematical models, Data models, Angular
velocity, Dynamic programming, Videos, symbolic
regression, NHU cells, evolutionary algorithms,
mathematical modelling",
-
DOI = "
doi:10.1109/ICDS62089.2024.10756371",
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abstract = "This paper introduces a novel approach to predicting
the number of cells in the Normal Human Urothelial
(NHU) environment in untreated monolayer cultures over
48 hours. We employ symbolic regression to develop a
mathematical model that delineates the relationship
between cell growth over time, migration speed, and
angular velocity. This data-driven technique
scrutinises the cell growth curve, a concept well-known
among biologists. Our methodology uses advanced
computer vision techniques to monitor time-lapse
videos, extracting detailed features such as cell
growth, mean migration speed, and angular velocity. A
significant innovation in our analysis is the
application of Genetic Programming for symbolic
regression, which enhances our ability to accurately
predict cellular behaviours under control conditions.
This approach allows the evolved network to precisely
identify features crucial for understanding the
responses of cells in untreated scenarios, providing
unbiased, efficient insights into cell dynamics. This
method supports the development of mathematical models
for computational simulations, enhancing our
understanding of cellular behaviours under normal
conditions.",
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notes = "Also known as \cite{10756371}
Institute for Mathematical Research, Universiti Putra
Malaysia, Selangor, Malaysia",
- }
Genetic Programming entries for
Nor Fadzillah Mohd Mokhtar
Syaffa Amir
Citations