Copyright ©The Author(s) 2020. Published by Baishideng Publishing Group Inc. All rights reserved.
Artif Intell Cancer. Jun 28, 2020; 1(1): 1-7
Published online Jun 28, 2020. doi: 10.35713/aic.v1.i1.1
Artificial intelligence and omics in cancer
Cédric Coulouarn
Cédric Coulouarn, Institut National de la Sante et de la Recherche Medicale (Inserm), Université de Rennes 1, Rennes F-35000, France
Author contributions: Coulouarn C solely contributed to this paper.
Supported by Inserm, Université de Rennes 1, Ligue Contre le Cancer, No. CD22, No. CD35, and No. CD85; INCa, and ITMO Cancer AVIESAN (Alliance Nationale pour les Sciences de la Vie et de la Santé) dans le cadre du Plan cancer (Non-coding RNA in cancerology: fundamental to translational), No. C18007NS.
Conflict-of-interest statement: No conflict-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: http://creativecommons.org/licenses/by-nc/4.0/
Corresponding author: Cédric Coulouarn, PhD, Senior Researcher, Principal Investigator, Team Leader, Institut National de la Sante et de la Recherche Medicale (Inserm), Université de Rennes 1, CHU Pontchaillou, 2 rue Henri Le Guilloux, Rennes F-35033, France. cedric.coulouarn@inserm.fr
Received: May 20, 2020
Peer-review started: May 20, 2020
First decision: June 4, 2020
Revised: June 9, 2020
Accepted: June 12, 2020
Article in press: June 12, 2020
Published online: June 28, 2020
Core Tip

Core tip: Artificial intelligence (AI) emerged in the field of cancer research as a new and promising discipline to improve the management of patients with cancer, including more accurate and fastest diagnosis to facilitate the therapeutic decision. AI models are mainly fueled by multi omics data. Integrating omics data and clinical data of patients represents a challenging but fascinating task.