Publication Name	Artificial Intelligence in Cancer
Manuscript ID	72421
Country	France

Received	2021-10-15 13:48
Peer-Review Started	2021-10-15 13:50
To Make the First Decision
Return for Revision	2021-10-24 00:25
Revised	2021-10-26 14:09
Second Decision	2021-10-27 11:58
Accepted by Journal Editor-in-Chief
Accepted by Company Editor-in-Chief	2021-10-27 21:13
Articles in Press	2021-10-27 21:13
Publication Fee Transferred
Edit the Manuscript by Language Editor
Typeset the Manuscript	2021-10-28 01:34
Publish the Manuscript Online	2021-10-28 08:03

ISSN	2644-3228 (online)
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: https://creativecommons.org/Licenses/by-nc/4.0/
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Category	Mathematical & Computational Biology
Manuscript Type	Minireviews
Article Title	Repairing the human with artificial intelligence in oncology
Manuscript Source	Invited Manuscript
All Author List	Ian Morilla
ORCID	Author(s) ORCID Number Ian Morilla http://orcid.org/0000-0002-5100-5990
Funding Agency and Grant Number
Corresponding Author	Ian Morilla, PhD, Assistant Professor, Senior Research Fellow, Laboratoire Analyse, Géométrie et Applications - Institut Galilée, Sorbonne Paris Nord University, 13 Sorbonne-Paris-Cité, Villetaneuse, Paris 75006, France. morilla@math.univ-paris13.fr
Key Words	Cancer research; Data analysis; Feature classification; Artificial intelligence; Machine learning; Healthcare systems
Core Tip	In this review, we explore powerful artificial intelligence based models enabling the comprehensive analysis of related problems on oncology. To this end, we described an asserted set of machine learning architectures that goes from the most classical multiple perceptron or neural networks to the novel federated and reinforcement learning designs. Overall, we point out the outgrowth of this mathematical discipline in cancer research and how computational biology and topological features can boost the general performances of these learning models.
Publish Date	2021-10-28 08:03
Citation	Morilla I. Repairing the human with artificial intelligence in oncology. Artif Intell Cancer 2021; 2(5): 60-68
URL	https://www.wjgnet.com/2644-3228/full/v2/i5/60.htm
DOI	https://dx.doi.org/10.35713/aic.v2.i5.60

Full Article (PDF)	AIC-2-60.pdf
Full Article (Word)	AIC-2-60.docx
Manuscript File	72421_Auto_Edited.docx
Answering Reviewers	72421-Answering reviewers.pdf
Audio Core Tip	72421-Audio core tip.m4a
Conflict-of-Interest Disclosure Form	72421-Conflict-of-interest statement.pdf
Copyright License Agreement	72421-Copyright license agreement.pdf
Non-Native Speakers of English Editing Certificate	72421-Language certificate.pdf
Peer-review Report	72421-Peer-review(s).pdf
Scientific Editor Work List	72421-Scientific editor work list.pdf