Artificial intelligence and machine learning could support drug development for hepatitis A virus internal ribosomal entry sites
Tatsuo Kanda, Reina Sasaki, Ryota Masuzaki, Mitsuhiko Moriyama, Division of Gastroenterology and Hepatology, Department of Medicine, Nihon University School of Medicine, Itabashi-ku 173-8610, Tokyo, Japan
Author contributions: Kanda T performed the majority of the writing and prepared the figures and tables; Sasaki R performed data acquisition and writing; Masuzaki R provided input in writing the paper; Moriyama M designed the outline and coordinated the writing of the paper; all authors have read and approve the final manuscript.
Supported by The Japan Agency for Medical Research and Development, No. JP20fk0210075.
Conflict-of-interest statement: There is no conflict of interest associated with any authors who contributed their efforts to this manuscript.
: 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: 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. email@example.com
Received: October 15, 2020
Peer-review started: October 15, 2020
First decision: December 17, 2020
Revised: December 29, 2020
Accepted: February 12, 2021
Article in press: February 12, 2021
Published online: February 28, 2021