Publication Name	Artificial Intelligence in Cancer
Manuscript ID	73891
Country	France

Received	2021-12-09 16:13
Peer-Review Started	2021-12-09 16:14
To Make the First Decision
Return for Revision	2022-01-26 18:38
Revised	2022-02-16 15:00
Second Decision	2022-04-27 02:38
Accepted by Journal Editor-in-Chief
Accepted by Executive Editor-in-Chief	2022-04-28 02:26
Articles in Press	2022-04-28 02:26
Publication Fee Transferred
Edit the Manuscript by Language Editor
Typeset the Manuscript	2022-04-28 09:20
Publish the Manuscript Online	2022-04-28 13:59

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: http://creativecommons.org/licenses/by-nc/4.0/
Copyright	©The Author(s) 2022. Published by Baishideng Publishing Group Inc. All rights reserved.
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Category	Mathematical & Computational Biology
Manuscript Type	Basic Study
Article Title	Learning models for colorectal cancer signature reconstruction and classification in patients with chronic inflammatory bowel disease
Manuscript Source	Unsolicited Manuscript
All Author List	Mariem Abaach and Ian Morilla
ORCID	Author(s) ORCID Number Mariem Abaach http://orcid.org/0000-0001-6855-7014 Ian Morilla http://orcid.org/0000-0002-5100-5990
Funding Agency and Grant Number	Funding Agency Grant Number Agence nationale de la recherche 10-LABX-0017
Corresponding Author	Ian Morilla, PhD, Assistant Professor, Research Associate, Laboratoire Analyse, Géométrie et Applications, Centre National de la Recherche Scientifique (Unité mixte de Recherche), Université Sorbonne Paris Nord, 99 avenue Jean Baptiste clément, Villetaneuse, Paris 93430, France. morilla@math.univ-paris13.fr
Key Words	Inflammatory bowel disease; microRNA; Muti-group comparison; Machine learning; Colorectal cancer; Sparse partial least squares-discriminant analysis
Core Tip	This study provides an optimised strategy based on classic learning methods and multi-group variable selection combination from 2600 microRNAs of 225 patients with one chronic inflammatory bowel disease to identify the minimal signature of microRNAs associated with the development of colorectal cancer in these patients.
Publish Date	2022-04-28 13:59
Citation	Abaach M, Morilla I. Learning Models for Colorectal Cancer Signature Reconstruction and Classification in Patients with Chronic Inflammatory Bowel Disease. Artif Intell Cancer 2022; 3(2): 27-41
URL	https://www.wjgnet.com/2644-3228/full/v3/i2/27.htm
DOI	https://dx.doi.org/10.35713/aic.v3.i2.27

Full Article (PDF)	AIC-3-27.pdf
Full Article (Word)	AIC-3-27.docx
Manuscript File	73891_Auto_Edited-LJH.docx
Answering Reviewers	73891-Answering reviewers.pdf
Audio Core Tip	73891-Audio core tip.m4a
Biostatistics Review Certificate	73891-Biostatistics statement.pdf
Conflict-of-Interest Disclosure Form	73891-Conflict-of-interest statement.pdf
Copyright License Agreement	73891-Copyright license agreement.pdf
Non-Native Speakers of English Editing Certificate	73891-Language certificate.pdf
Supplementary Material	73891-Supplementary material.pdf
Peer-review Report	73891-Peer-review(s).pdf
Scientific Misconduct Check	73891-Bing-Liu JH-2.jpg
Scientific Misconduct Check	73891-CrossCheck.jpg
Scientific Editor Work List	73891-Scientific editor work list.pdf
CrossCheck Report	73891-CrossCheck report.pdf