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
Core Tip

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