Published online Nov 28, 2020. doi: 10.35713/aic.v1.i4.51
Peer-review started: September 26, 2020
First decision: October 22, 2020
Revised: November 5, 2020
Accepted: November 21, 2020
Article in press: November 21, 2020
Published online: November 28, 2020
Understanding of the cellular signaling pathways involved in cancer disease is of great importance. These complex biological mechanisms can be thoroughly revealed by their structure, dynamics, and control methods. Artificial intelligence offers rule-based models that favor the research of human signaling processes. In this paper, we give an overview of the advantages of the formalism of symbolic models in medical biology and cell biology of the uveal melanoma. A language is described that allows us: (1) To define the system states and elements with their alterations; (2) To model the dynamics of the cellular system; and (3) To perform inference-based analysis with the logical tools of the language.
Core Tip: Artificial intelligence offers rule-based models that favor the understanding of cell biology (signaling pathways) involved in the uveal melanoma.