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Articles Published Processes
9/2/2026 6:35:30 AM | Browse: 0 | Download: 0
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Received |
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2026-04-20 08:31 |
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Peer-Review Started |
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2026-04-20 08:32 |
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First Decision by Editorial Office Director |
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Return for Revision |
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2026-06-22 09:11 |
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Revised |
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2026-07-02 06:51 |
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Publication Fee Transferred |
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Second Decision by Editor |
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2026-08-14 02:39 |
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Second Decision by Editor-in-Chief |
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Final Decision by Editorial Office Director |
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2026-08-14 09:13 |
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Articles in Press |
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2026-08-14 09:13 |
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Edit the Manuscript by Language Editor |
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2026-08-17 03:18 |
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Typeset the Manuscript |
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2026-08-27 00:27 |
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Publish the Manuscript Online |
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2026-09-02 06:35 |
| ISSN |
2644-3228 (online) |
| Open Access |
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial (CC BY-NC 4.0) license. No commercial re-use. See Permissions. Published by Baishideng Publishing Group Inc. |
| Copyright |
©Author(s) (or their employer(s)) 2026. No commercial re-use. See Permissions. Published by Baishideng Publishing Group Inc. |
| 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 |
Oncology |
| Manuscript Type |
Minireviews |
| Article Title |
Artificial intelligence-driven decoding of trajectories of cancer cell plasticity: A paradigm shift in oncological assessment and treatment
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| Manuscript Source |
Invited Manuscript |
| All Author List |
Mansi Srivastava and Minal Garg |
| ORCID |
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| Funding Agency and Grant Number |
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| Corresponding Author |
Minal Garg, Full Professor, Department of Biochemistry, University of Lucknow, University Road, Lucknow 226007, Uttar Pradesh, India. garg_minal@lkouniv.ac.in |
| Key Words |
Artificial intelligence; Cellular plasticity; Diagnostics and therapeutics; (Epi)genome and transcriptomic landscape; Tumor heterogeneity |
| Core Tip |
Dynamic switching between cellular states generates tumoral heterogeneity as explained by stochastic and hierarchy models, and is driven by genetic and epigenetic alterations in a tumor ecosystem. Recent advancements in the applications of artificial intelligence-driven deep learning and machine learning models integrated with next-generation sequencing and single-cell RNA analysis lead to the elucidation of the changes in the trajectories of cellular plasticity. artificial intelligence-powered approaches decipher the (epi)genomic and transcriptomic landscape associated with normal, metaplastic, and pre-malignant tissues and thus help in establishing the new predictive signatures for tailored diagnosis, risk assessments, accelerating drug discovery and individualizing treatment regimens. |
| Publish Date |
2026-09-02 06:35 |
| Citation |
Srivastava M, Garg M. Artificial intelligence-driven decoding of trajectories of cancer cell plasticity: A paradigm shift in oncological assessment and treatment. Artif Intell Cancer 2026; 7(1): 122429
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| URL |
https://www.wjgnet.com/2644-3228/full/v7/i1/122429.htm |
| DOI |
https://doi.org/10.35713/aic.122429 |
Copyright © 1993-2026 Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA. All rights reserved, including rights relating to text and data mining, AI training, and similar technologies. For open-access content, the applicable copyright and licensing terms govern.
