Copyright ©The Author(s) 2020. Published by Baishideng Publishing Group Inc. All rights reserved.
Artif Intell Cancer. Nov 28, 2020; 1(4): 51-65
Published online Nov 28, 2020. doi: 10.35713/aic.v1.i4.51
Artificial intelligence for modeling uveal melanoma
Beatriz Santos-Buitrago, Gustavo Santos-García, Emiliano Hernández-Galilea
Beatriz Santos-Buitrago, Bio and Health Informatics Lab, Seoul National University, Seoul 151-742, South Korea
Gustavo Santos-García, IME, University of Salamanca, Salamanca 37007, Spain
Gustavo Santos-García, FADoSS Research Unit, Universidad Complutense de Madrid, Madrid 28040, Spain
Emiliano Hernández-Galilea, Department of Ophthalmology, Institute of Biomedicine Investigation of Salamanca (IBSAL), University Hospital of Salamanca, University of Salamanca, Salamanca 37007, Spain
Author contributions: The authors contributed equally to this work.
Conflict-of-interest statement: The authors declare no conflicts of interest.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See:
Corresponding author: Gustavo Santos-García, PhD, Professor, IME, University of Salamanca, FES Building, Campus Miguel de Unamuno, Salamanca 37007, Spain.
Received: September 26, 2020
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.

Keywords: Uveal melanoma, Signal transduction, Pathway Logic, Symbolic systems biology, Artificial intelligence

Core Tip: Artificial intelligence offers rule-based models that favor the understanding of cell biology (signaling pathways) involved in the uveal melanoma.