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Articles Published Processes
2/25/2021 4:24:19 AM | Browse: 698 | Download: 1633
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Received |
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2020-10-15 12:11 |
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Peer-Review Started |
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2020-10-15 12:11 |
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First Decision by Editorial Office Director |
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2020-12-17 19:33 |
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Return for Revision |
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2020-12-17 19:33 |
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Revised |
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2020-12-29 06:58 |
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Publication Fee Transferred |
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Second Decision by Editor |
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2021-02-03 03:36 |
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Second Decision by Editor-in-Chief |
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Final Decision by Editorial Office Director |
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2021-02-12 18:46 |
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Articles in Press |
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2021-02-12 18:46 |
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Edit the Manuscript by Language Editor |
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Typeset the Manuscript |
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2021-02-19 09:35 |
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Publish the Manuscript Online |
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2021-02-25 04:24 |
| ISSN |
2644-3236 (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) 2021. Published by Baishideng Publishing Group Inc. All rights reserved. |
| Article Reprints |
For details, please visit: http://www.wjgnet.com/bpg/gerinfo/247
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| Permissions |
For details, please visit: http://www.wjgnet.com/bpg/gerinfo/207
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| Publisher |
Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA |
| Website |
http://www.wjgnet.com |
| Category |
Gastroenterology & Hepatology |
| Manuscript Type |
Minireviews |
| Article Title |
Artificial intelligence and machine learning could support drug development for hepatitis A virus internal ribosomal entry sites
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| Manuscript Source |
Invited Manuscript |
| All Author List |
Tatsuo Kanda, Reina Sasaki, Ryota Masuzaki and Mitsuhiko Moriyama |
| ORCID |
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| Funding Agency and Grant Number |
| Funding Agency |
Grant Number |
| The Japan Agency for Medical Research and Development |
JP20fk0210075 |
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| Corresponding Author |
Tatsuo Kanda, MD, PhD, Associate Professor, Division of Gastroenterology and Hepatology, Department of Medicine, Nihon University School of Medicine, 30-1 Oyaguchi-kamicho, Itabashi-ku 173-8610, Tokyo, Japan. kanda.tatsuo@nihon-u.ac.jp |
| Key Words |
Artificial intelligence; Hepatitis A virus internal ribosomal entry sites; Cap-independent translation; Antivirals; Severe hepatitis A; Glucose-regulated protein 78 |
| Core Tip |
In certain areas, it is difficult to perform universal hepatitis A virus (HAV) vaccination. We found that several drugs potentially inhibit HAV internal ribosomal entry sites-dependent translation and HAV replication. After the application of machine and deep learning, artificial intelligence identified effective anti-HAV drugs more quickly, using drug repositioning and drug rescue. |
| Publish Date |
2021-02-25 04:24 |
| Citation |
Kanda T, Sasaki R, Masuzaki R, Moriyama M. Artificial intelligence and machine learning could support drug development for hepatitis A virus internal ribosomal entry sites. Artif Intell Gastroenterol 2021; 2(1): 1-9 |
| URL |
https://www.wjgnet.com/2644-3236/full/v2/i1/1.htm |
| DOI |
https://dx.doi.org/10.35712/aig.v2.i1.1 |
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