| Abstrakt: |
A study conducted at TED University in Ankara, Turkey, investigated a numerical and statistical approach to understanding cancer cell invasion of tissue. The researchers used a one-dimensional chemotaxis-haptotaxis model and solved the governing equations using the radial basis function collocation method. They also employed machine learning approaches to model the mean density of cancer cells, extracellular matrix, and urokinase plasminogen activator. The results showed that the trilayer neural network model performed better than the multilayer adaptive regression splines model. This research provides valuable insights into the mechanisms of cancer cell invasion. [Extracted from the article] |
